Systems and methods for subterranean fluid flow characterization

ABSTRACT

A method of monitoring fluid outflow along a wellbore includes obtaining an acoustic signal from a sensor within the wellbore. The acoustic signal includes acoustic samples across a portion of a depth of the wellbore. In addition, the method includes determining one or more frequency domain features from the acoustic signal. Further, the method includes identifying one or more fluid outflow locations along the portion of the depth of the wellbore using the one or more frequency domain features.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.17/330,117, Filed May 25, 2021, and entitled “SYSTEMS AND METHODS FORSUBTERRANEAN FLUID FLOW CHARACTERIZATION,” which claims the benefit ofand priority to International Application No. PCT/EP2020/066171 filedJun. 11, 2020 with the European Receiving Office and entitled “Systemsand Methods for Subterranean Fluid Flow Characterization,” each of whichis hereby incorporated herein by reference in its entirety for allpurposes.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

Not applicable.

BACKGROUND

To access hydrocarbons disposed within a subterranean formation, awellbore is drilled from the surface into the formation. Thereafter,formation fluids (e.g., such as hydrocarbon liquids, hydrocarbon gases,water, etc.) may be flowed into the wellbore and communicated to thesurface. In addition, in some instances, a fluid (e.g., water, steam,acid, etc.) may be injected into the formation via the wellbore so as toincrease a pressure of the formation, adjust a porosity or permeabilityof the formation, decrease a viscosity of the hydrocarbons disposedwithin the formation, etc. Thus, during the operating life of asubterranean wellbore, various fluid flows both into and/or out of thewellbore may be present.

BRIEF SUMMARY

In an aspect, a method of monitoring fluid outflow along a wellborecomprises obtaining an acoustic signal from a sensor within thewellbore, determining one or more frequency domain features from theacoustic signal, and identifying one or more fluid outflow locationsalong the portion of the depth of the wellbore using the one or morefrequency domain features. The acoustic signal comprises acousticsamples across a portion of a depth of the wellbore.

In an aspect, a method of monitoring an injection of fluid into asubterranean formation comprises obtaining one or more frequency domainfeatures from an acoustic signal originating within a wellbore extendinginto the subterranean formation, identifying one or more fluid outflowlocations within the wellbore using the one or more frequency domainfeatures, obtaining one or more temperature features from a temperaturesignal originating within the wellbore, and identifying one or morefluid uptake locations within the subterranean formation using thetemperature features within the wellbore.

In an aspect, a method of monitoring fluid outflow along a wellborecomprises determining one or more temperature features from adistributed temperature signal originating in the wellbore, determiningone or more frequency domain features from an acoustic signaloriginating in the wellbore, and using the one or more temperaturefeatures and the one or more frequency domain features to identify oneor more fluid outflow locations along the wellbore.

In an aspect, a method of monitoring fluid injection into a subterraneanformation comprises obtaining a first acoustic signal from a firstsensor within a first wellbore, determining one or more frequency domainfeatures from the first acoustic signal, identifying one or more fluidoutflow locations within the first wellbore using the one or morefrequency domain features from the first acoustic signal, obtaining asecond acoustic signal from a second sensor within a second wellbore,determining one or more frequency domain features from the secondacoustic signal, and identifying one or more fluid inflow locationswithin the second wellbore using the one or more frequency domainfeatures from the second acoustic signal. The first acoustic signalcomprises acoustic samples across a portion of a depth of the firstwellbore, and the second acoustic signal comprises acoustic samplesacross a portion of a depth of the second wellbore.

In an aspect, a method for monitoring fluid injection into asubterranean formation comprises injecting a fluid into a wellboreextending into the subterranean formation, receiving an acoustic signalfrom a sensor within the wellbore, determining one or more frequencydomain features from the acoustic signal, determining an allocation ofan injected volume of the fluid across a plurality of outflow locationsusing the one or more frequency domain features, receiving, at a firsttime, an indication of a change in the allocation, storing a portion ofthe acoustic signal as a result of receiving the indication of thechange, wherein the portion includes the first time, and identifying anevent within the subterranean formation using the portion of theacoustic signal. The acoustic signal comprises acoustic samples across aportion of a depth of the wellbore.

Embodiments described herein comprise a combination of features andcharacteristics intended to address various shortcomings associated withcertain prior devices, systems, and methods. The foregoing has outlinedrather broadly the features and technical characteristics of thedisclosed embodiments in order that the detailed description thatfollows may be better understood. The various characteristics andfeatures described above, as well as others, will be readily apparent tothose skilled in the art upon reading the following detaileddescription, and by referring to the accompanying drawings. It should beappreciated that the conception and the specific embodiments disclosedmay be readily utilized as a basis for modifying or designing otherstructures for carrying out the same purposes as the disclosedembodiments. It should also be realized that such equivalentconstructions do not depart from the spirit and scope of the principlesdisclosed herein.

BRIEF DESCRIPTION OF THE DRAWINGS

For a detailed description of various exemplary embodiments, referencewill now be made to the accompanying drawings in which:

FIG. 1 is a schematic, cross-sectional illustration of a downholewellbore operating environment according to some embodiments;

FIGS. 2A and 2B are different example cross-sectional views of awellbore of the wellbore operating environment of FIG. 1 according tosome embodiments;

FIG. 3 is a schematic, cross-sectional illustration of a well systemaccording to some embodiments;

FIG. 4 is a flow diagram of a method of characterizing fluid inflows andoutflows into and from a wellbore according to some embodiments;

FIG. 5 is a flow diagram of a method of characterizing a fluid flowoutflow from a wellbore and into a subterranean formation according tosome embodiments;

FIG. 6 is a flow diagram of a method of characterizing a fluid outflowfrom a wellbore based on an acoustic signal and a temperature signalwithin the wellbore according to some embodiments;

FIG. 7 is a flow diagram of a method of characterizing fluid flows of afluid injection operation between a pair of wellbores extending within asubterranean formation according to some embodiments;

FIG. 8 is a flow diagram of a method of identifying an event within asubterranean formation according to some embodiments;

FIG. 9 is a flow diagram of a method of developing a fluid flow modelaccording to some embodiments;

FIG. 10A is a schematic illustration of a flow loop assembly utilized totrain an fluid flow model according to some embodiments;

FIG. 10B is a schematic showing wellbore depths corresponding toemission or injection points of FIG. 10A; and

FIG. 11 schematically illustrates a computer that may be used to carryout various methods according to some embodiments.

DETAILED DESCRIPTION

The following discussion is directed to various exemplary embodiments.However, one of ordinary skill in the art will understand that theexamples disclosed herein have broad application, and that thediscussion of any embodiment is meant only to be exemplary of thatembodiment, and not intended to suggest that the scope of thedisclosure, including the claims, is limited to that embodiment.

The drawing figures are not necessarily to scale. Certain features andcomponents herein may be shown exaggerated in scale or in somewhatschematic form and some details of conventional elements may not beshown in interest of clarity and conciseness.

Unless otherwise specified, any use of any form of the terms “connect,”“engage,” “couple,” “attach,” or any other term describing aninteraction between elements is not meant to limit the interaction todirect interaction between the elements and may also include indirectinteraction between the elements described. In the following discussionand in the claims, the terms “including” and “comprising” are used in anopen-ended fashion, and thus should be interpreted to mean “including,but not limited to . . . ”. Reference to up or down will be made forpurposes of description with “up,” “upper,” “upward,” “upstream,” or“above” meaning toward the surface of the wellbore and with “down,”“lower,” “downward,” “downstream,” or “below” meaning toward theterminal end of the well, regardless of the wellbore orientation.Reference to inner or outer will be made for purposes of descriptionwith “in,” “inner,” or “inward” meaning towards the central longitudinalaxis of the wellbore and/or wellbore tubular, and “out,” “outer,” or“outward” meaning towards the wellbore wall. As used herein, the term“longitudinal” or “longitudinally” refers to an axis substantiallyaligned with the central axis of the wellbore tubular, and “radial” or“radially” refer to a direction perpendicular to the longitudinal axis.The various characteristics mentioned above, as well as other featuresand characteristics described in more detail below, will be readilyapparent to those skilled in the art with the aid of this disclosureupon reading the following detailed description of the embodiments, andby referring to the accompanying drawings.

As previously described, during the operational life of a subterraneanwellbore, various fluid flows into and/or out of the wellbore may occur.It is desirable to identify and characterize the fluid flows within thewellbore so as to facilitate more effective management of the wellbore.For instance, a well operator may wish the know where fluid enters orexits the wellbore and at what flow rates or amounts so as to ensurethat fluid is flowing as desired within the wellbore and also within thesurrounding formation during operations.

Accordingly, embodiments disclosed herein provide systems and methods ofcharacterizing fluid flow within a subterranean formation. Specifically,in some embodiments, the disclosed systems and methods may be used tocontinuously identify and characterize fluid inflow and/or outflow froma subterranean wellbore from or into, respectively, a subterraneanformation. In some embodiments, the disclosed systems and methods may beused to characterize fluid flow within a subterranean formationfollowing injection or outflow from the subterranean wellbore. In someembodiments, the disclosed systems and methods may be used to identifyand characterize events within the subterranean formation, such as, forinstance micro-seismic events.

In some instances, the systems and methods can provide information inreal time or near real time. As used herein, the term “real time” refersto a time that takes into account various communication and latencydelays within a system, and can include actions taken within about tenseconds, within about thirty seconds, within about a minute, withinabout five minutes, or within about ten minutes of the action occurring.Various sensors (e.g., distributed fiber optic acoustic sensors, pointsensors, etc.) can be used to obtain a suitable sampling or measurementat various points along the wellbore. In some embodiments, the samplingor measurement may comprise an acoustic signal, a temperature signal, orboth. The acoustic and/or temperature signals can be processed usingsignal processing architecture with various feature extractiontechniques to obtain a measure of one or more frequency domain featuresand/or one or more temperature features. While discussed in terms ofbeing real time in some instances, the data can also be analyzed at alater time at the same location and/or a displaced location.

For embodiments that utilize an acoustic signal to characterizesubterranean fluid flows, various frequency domain features can beobtained from the acoustic signal, and in some contexts, the frequencydomain features can also be referred to herein as spectral features orspectral descriptors. The frequency domain features are featuresobtained from the frequency domain analysis of the acoustic signalsobtained within the wellbore, where the acoustic signal can be furtherresolved into depth intervals or sections using time of flightmeasurements from returned or reflected signals in an optical fiber. Thefrequency domain features can be derived from the full spectrum of thefrequency domain of the acoustic signal such that each of the frequencydomain features can be representative of the frequency spectrum of theacoustic signal. Further, a plurality of different frequency domainfeatures can be obtained from the same acoustic signal, where each ofthe different frequency domain features is representative of frequenciesacross the same frequency spectrum of the acoustic signal as the otherfrequency domain features. For example, the frequency domain features(e.g., each frequency domain feature) can be statistical shapemeasurement or spectral shape function of the spectral power measurementacross the same frequency bandwidth of the acoustic signal. Further, asused herein, frequency domain features can also refer to features orfeature sets derived from one or more frequency domain features,including combinations of features, mathematical modifications to theone or more frequency domain features, rates of change of the one ormore frequency domain features, and the like.

In some embodiments, the spectral features can comprise other features,including those in the time domain, various transforms (e.g., wavelets,Fourier transforms, etc.), and/or those derived from portions of theacoustic signal or other sensor inputs. Such other features can be usedon their own or in combination one or more frequency domain features,including in the development of transformations of the features, asdescribed in more detail herein.

In some embodiments, the acoustic signal(s) can be obtained in a mannerthat allows for a signal to be obtained along the entire wellbore or aportion of interest. Specifically, some embodiments may make use offiber optic distributed acoustic sensors (DAS) to capture acousticsignals resulting from fluid flowing into and/or out of a subterraneanwellbore along an entire length or some designated length of thewellbore. After applying suitable signal processing procedures (e.g.,such as those described herein), fluid inflow, outflow, and flow signalsmay be distinguished from other noise sources to properly identify andcharacterize each type of event.

For embodiments that utilize a temperature signal to characterizesubterranean fluid flows various temperature features can be derivedfrom temperature measurements within a subterranean wellbore. Fiberoptic distributed temperature sensors (DTS) can capture distributedtemperature sensing signals resulting from downhole events, such aswellbore events (e.g., fluid inflow/outflow, leaks, overburden movement,and the like), as well as other background events. This allows forsignal processing procedures that distinguish events and flow signalsfrom other sources to properly identify each type of event. This in turnresults in a need for a clearer understanding of the fingerprint ofin-well event of interest (e.g., fluid inflow, fluid outflow, fluid flowalong the tubulars, etc.) in order to be able to segregate and identifya signal resulting from an event of interest from other ambientbackground signals. As used herein, the resulting fingerprint of aparticular event can also be referred to as an event signature, asdescribed in more detail herein. In some embodiments, the temperaturefeatures can be used with a model (e.g., a machine learning model,multivariate model, etc.) to provide for detection, identification, anddetermination of the various events. A number of different models can bedeveloped and used to determine when certain events have occurred, forexample, within a wellbore.

Referring now to FIG. 1 , a schematic, cross-sectional illustration of adownhole wellbore operating environment 101 according to someembodiments is shown. More specifically, environment 101 includes awellbore 114 traversing a subterranean formation 102, casing 112 liningat least a portion of wellbore 114, and a tubular 120 extending throughwellbore 114 and casing 112. A plurality of completion assemblies suchas spaced screen elements or assemblies 118 may be provided alongtubular 120 at one or more production zones 104 a, 104 b within thesubterranean formation 102. In particular, two production zones 104 a,104 b are depicted within subterranean formation 102 of FIG. 1 ;however, the precise number and spacing of the production zones 104 a,104 b may be varied in different embodiments. The completion assembliescan comprise flow control devices such as sliding sleeves, adjustablechokes, and/or inflow control devices to allow for control of the flowfrom each production zone 104 a, 104 b. The production zones 104 a, 104b may be layers, zones, or strata of formation 102 that containhydrocarbon fluids (e.g., oil, gas, condensate, etc.) therein.

In addition, a plurality of spaced zonal isolation devices 117 andgravel packs 122 may be provided between tubular 120 and the sidewall ofwellbore 114 (i.e., within the annulus 119) at or along the interface ofthe wellbore 114 with the production zones 104 a, 104 b. In someembodiments, the operating environment 101 includes a workover and/ordrilling rig positioned at the surface and extending over the wellbore114. While FIG. 1 shows an example completion configuration in FIG. 1 ,it should be appreciated that other configurations and equipment may bepresent in place of or in addition to the illustrated configurations andequipment. For example, sections of the wellbore 114 can be completed asopen hole completions or with gravel packs without completionassemblies.

In general, the wellbore 114 can be formed in the subterranean formation102 using any suitable technique (e.g., drilling). The wellbore 114 canextend substantially vertically from the earth's surface over a verticalwellbore portion, deviate from vertical relative to the earth's surfaceover a deviated wellbore portion, and/or transition to a horizontalwellbore portion. In general, all or portions of a wellbore may bevertical, deviated at any suitable angle, horizontal, and/or curved. Inaddition, the wellbore 114 can be a new wellbore, an existing wellbore,a straight wellbore, an extended reach wellbore, a sidetracked wellbore,a multi-lateral wellbore, and other types of wellbores for drilling andcompleting one or more production zones. As illustrated, the wellbore114 includes a substantially vertical producing section 150 whichincludes the production zones 104 a, 104 b. In this embodiment,producing section 150 is an open-hole completion (i.e., casing 112 doesnot extend through producing section 150). Although section 150 isillustrated as a vertical and open-hole portion of wellbore 114 in FIG.1 , embodiments disclosed herein can be employed in sections ofwellbores having any orientation, and in open or cased sections ofwellbores. The casing 112 extends into the wellbore 114 from the surfaceand can be secured within the wellbore 114 with cement 111.

The tubular 120 may comprise any suitable downhole tubular or tubularstring (e.g., drill string, casing, liner, jointed tubing, and/or coiledtubing, etc.), and may be inserted within wellbore 114 for any suitableoperation(s) (e.g., drilling, completion, intervention, workover,treatment, production, etc.). In the embodiment shown in FIG. 2 , thetubular 120 is a completion assembly string. In addition, the tubular120 may be disposed within in any or all portions of the wellbore 114(e.g., vertical, deviated, horizontal, and/or curved section of wellbore114).

In this embodiment, the tubular 120 extends from the surface to theproduction zones 104 a, 104 b and generally provides a conduit forfluids to travel from the formation 102 (particularly from productionzones 104 a, 104 b) to the surface. A completion assembly including thetubular 120 can include a variety of other equipment or downhole toolsto facilitate the production of the formation fluids from the productionzones. For example, zonal isolation devices 117 can be used to isolatethe production zones 104 a, 104 b within the wellbore 114. In thisembodiment, each zonal isolation device 117 comprises a packer (e.g.,production packer, gravel pack packer, frac-pac packer, etc.). The zonalisolation devices 117 can be positioned between the screen assemblies118, for example, to isolate different gravel pack zones or intervalsalong the wellbore 114 from each other. In general, the space betweeneach pair of adjacent zonal isolation devices 117 defines a productioninterval, and each production interval may corresponding with one of theproduction zones 104 a, 104 b of subterranean formation 102.

The screen assemblies 118 provide sand control capability. Inparticular, the sand control screen elements 118, or other filter mediaassociated with wellbore tubular 120, can be designed to allow fluids toflow therethrough but restrict and/or prevent particulate matter ofsufficient size from flowing therethrough. The screen assemblies 118 canbe of any suitable type such as the type known as “wire-wrapped”, whichare made up of a wire closely wrapped helically about a wellboretubular, with a spacing between the wire wraps being chosen to allowfluid flow through the filter media while keeping particulates that aregreater than a selected size from passing between the wire wraps. Othertypes of filter media can also be provided along the tubular 120 and caninclude any type of structures commonly used in gravel pack wellcompletions, which permit the flow of fluids through the filter orscreen while restricting and/or blocking the flow of particulates (e.g.other commercially-available screens, slotted or perforated liners orpipes; sintered-metal screens; sintered-sized, mesh screens; screenedpipes; prepacked screens and/or liners; or combinations thereof). Aprotective outer shroud having a plurality of perforations therethroughmay be positioned around the exterior of any such filter medium.

The gravel packs 122 can be formed in the annulus 119 between the screenelements 118 (or tubular 120) and the sidewall of the wellbore 114 in anopen hole completion. In general, the gravel packs 122 compriserelatively coarse granular material placed in the annulus to form arough screen against the ingress of sand into the wellbore while alsosupporting the wellbore wall. The gravel pack 122 is optional and maynot be present in all completions.

In some embodiments, one or more of the completion assemblies cancomprise flow control elements such as sliding sleeves, chokes, valves,or other types of flow control devices that can control the flow of afluid from an individual production zone or a group of production zones.The force on the production face can then vary based on the type ofcompletion within the wellbore and/or each production zone (e.g., in asliding sleeve completion, open hole completion, gravel pack completion,etc.). In some embodiments, a sliding sleeve or other flow controlledproduction zone can experience a force on the production face that isrelatively uniform within the production zone, and the force on theproduction face can be different between each production zone. Forexample, a first production zone can have a specific flow controlsetting that allows the production rate from the first zone to bedifferent than the production rate from a second production zone. Thus,the choice of completion type (e.g., which can be specified in acompletion plan) can effect on the need for or the ability to provide adifferent production rate within different production zones.

Referring still to FIG. 1 , a monitoring system 110 can comprise anacoustic monitoring system and/or a temperature monitoring system. Themonitoring system 110 can be positioned in the wellbore 114. Asdescribed herein, the monitoring system 110 may be utilized to detectand/or characterize fluid flow event(s) (e.g., fluid inflow or outflowevents) within wellbore 114.

The monitoring system 110 comprises an optical fiber 162 that is coupledto and extends along tubular 120. In cased completions, the opticalfiber 162 can be installed between the casing and the wellbore wallwithin a cement layer and/or installed within the casing or productiontubing. Referring briefly to FIGS. 2A and 2B, optical fiber 162 of themonitoring system 110 may be coupled to an exterior of tubular 120(e.g., such as shown in FIG. 2B) or an interior of tubular (e.g., suchas shown in FIG. 2A). When the optical fiber 162 is coupled to theexterior of the tubular 120, as depicted in the embodiment of FIG. 2B,the optical fiber 162 can be positioned within a control line, controlchannel, or recess in the tubular 120. In some embodiments an outershroud contains the tubular 120 and protects the optical fiber 162during installation. A control line or channel can be formed in theshroud and the optical fiber 162 can be placed in the control line orchannel (not specifically shown in FIGS. 2A and 2B).

Referring again to FIG. 1 , generally speaking, during operation of athe monitoring system, an optical backscatter component of lightinjected into the optical fiber 162 may be used to detect variousconditions incident on the optical fiber such as acoustic perturbations(e.g., dynamic strain), temperature, static strain, and the like alongthe length of the optical fiber 162. The light can be generated by alight generator or source 166 such as a laser, which can generate lightpulses. The light used in the system is not limited to the visiblespectrum, and light of any frequency can be used with the systemsdescribed herein. Accordingly, the optical fiber 162 acts as the sensorelement with no additional transducers in the optical path, andmeasurements can be taken along the length of the entire optical fiber162. The measurements can then be detected by an optical receiver suchas sensor 164 and selectively filtered to obtain measurements from agiven depth point or range, thereby providing for a distributedmeasurement that has selective data for a plurality of zones (e.g.,production zones 104 a, 104 b) along the optical fiber 162 at any giventime. For example, time of flight measurements of the backscatteredlight can be used to identify individual zones or measurement lengths ofthe fiber optic 162. In this manner, the optical fiber 162 effectivelyfunctions as a distributed array of sensors spread over the entirelength of the optical fiber 162, which typically across production zones104 a, 104 b within the wellbore 114.

The light backscattered up the optical fiber 162 as a result of theoptical backscatter can travel back to the source, where the signal canbe collected by a sensor 164 and processed (e.g., using a processor168). In general, the time the light takes to return to the collectionpoint is proportional to the distance traveled along the optical fiber162, thereby allowing time of flight measurements of distance along theoptical fiber. The resulting backscattered light arising along thelength of the optical fiber 162 can be used to characterize theenvironment around the optical fiber 162. The use of a controlled lightsource 166 (e.g., having a controlled spectral width and frequency) mayallow the backscatter to be collected and any parameters and/ordisturbances along the length of the optical fiber 162 to be analyzed.In general, the various parameters and/or disturbances along the lengthof the optical fiber 162 can result in a change in the properties of thebackscattered light.

An acquisition device 160 may be coupled to one end of the optical fiber162 that comprises the sensor 164, light generator 166, a processor 168,and a memory 170. As discussed herein, the light source 166 can generatethe light (e.g., one or more light pulses), and the sensor 164 cancollect and analyze the backscattered light returning up the opticalfiber 162. In some contexts, the acquisition device 160 (which comprisesthe light source 166 and the sensor 164 as noted above), can be referredto as an interrogator. The processor 168 may be in signal communicationwith the sensor 164 and may perform various analysis steps described inmore detail herein. While shown as being within the acquisition device160, the processor 168 can also be located outside of the acquisitiondevice 160 including being located remotely from the acquisition device160. The sensor 164 can be used to obtain data at various rates and mayobtain data at a sufficient rate to detect the acoustic signals ofinterest with sufficient bandwidth. While described as a sensor 164 in asingular sense, the sensor 164 can comprise one or more photodetectorsor other sensors that can allow one or more light beams and/orbackscattered light to be detected for further processing. In anembodiment, depth resolution ranges in a range of from about 1 meter toabout 10 meters, or less than or equal to about 10, 9, 8, 7, 6, 5, 4, 3,2, or 1 meter can be achieved. Depending on the resolution needed,larger averages or ranges can be used for computing purposes. When ahigh depth resolution is not needed, a system may have a widerresolution (e.g., which may be less expensive) can also be used in someembodiments. Data acquired by the monitoring system 110 (e.g., via fiber162, sensor 164, etc.) may be stored on memory 170.

The monitoring system 110 can be used for detecting a variety ofparameters and/or disturbances in the wellbore including being used todetect temperatures along the wellbore, acoustic signals along thewellbore, static strain and/or pressure along the wellbore, or anycombination thereof.

In some embodiments, the monitoring system 110 may comprise a DTSsystem. Specifically, the monitoring system 110 may rely on lightinjected into the optical fiber 162 along with the reflected signals todetermine a temperature along the optical fiber 162 based on opticaltime-domain reflectometry. In some embodiments, the monitoring system110 may comprise a DAS system that may rely on light injected into theoptical fiber 162 along with the reflected signals to capture acousticperturbations (e.g., dynamic strain) along the length of the fiber 162.

In order to obtain DTS and/or DAS measurements, a pulsed laser from thelight generator 166 can be coupled to the optical fiber 162 that servesas the sensing element. The injected light can be backscattered as thepulse propagates through the optical fiber 162 owing to density andcomposition as well as to molecular and bulk vibrations. A portion ofthe backscattered light can be guided back to the acquisition device 160and split of by a directional coupler to a sensor 164. It is expectedthat the intensity of the backscattered light decays exponentially withtime. As the speed of light within the optical fiber 162 is known, thedistance that the light has passed through the optical fiber 162 can bederived using time of flight measurements.

In both DAS and DTS systems (e.g., such as those that may be includedwithin monitoring system 110), the backscattered light includesdifferent spectral components which contain peaks that are known asRayleigh and Brillouin peaks and Raman bands. The Rayleigh peaks areindependent of temperature and can be used to determine the DAScomponents of the backscattered light. The Raman spectral bands arecaused by thermally influenced molecular vibrations. The Raman spectralbands can then be used to obtain information about distribution oftemperature along the length of the optical fiber 162 disposed in thewellbore.

The Raman backscattered light has two components, Stokes andAnti-Stokes, one being only weakly dependent on temperature and theother being greatly influenced by temperature. The relative intensitiesbetween the Stokes and Anti-Stokes components and are a function oftemperature at which the backscattering occurred. Therefore, temperaturecan be determined at any point along the length of the optical fiber 162by comparing at each point the Stokes and Anti-stokes components of thelight backscattered from the particular point. The Brillouin peaks maybe used to monitor strain along the length of the optical fiber 162.

The monitoring system 110 may comprise DTS system, a DAS system, or acombined DTS and DAS system. In some embodiments, more than oneacquisition devices 160 may be coupled to a single optical fiber 162such that at least of the acquisition devices 160 is to interpretbackscattered light emitted through the optical fiber 162 fordistributed acoustic signals, and at least one of the acquisitiondevices 160 is to interpret backscattered light emitted through theoptical fiber 162 for distributed temperature signals. In someembodiments, a single acquisition device 160 may support both DAS andDTS functionality within the monitoring system 110. In some embodiments,a plurality of fibers 162 are present within the wellbore, and the DASsystem can be coupled to a first optical fiber and the DTS system can becoupled to a second, different, optical fiber. In some embodiments, asingle optical fiber can be used with both systems, and a time divisionmultiplexing or other process can be used to measure both DAS and DTS onthe same optical fiber.

In an embodiment, depth resolution for the monitoring system 110 (e.g.,functioning as a DAS and/or DTS system as described above) can rangefrom about 1 meter to about 10 meters, or less than or equal to about10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 meter can be achieved. Depending on theresolution needed, larger averages or ranges can be used for computingpurposes. When a high depth resolution is not needed, a system may havea wider resolution (e.g., which may be less expensive) can also be usedin some embodiments. Data acquired by the monitoring system 110 (e.g.,via fiber 162, sensor 164, etc.) may be stored on memory 170.

While the monitoring system 110 described herein can use a temperatureand/or acoustic measurements for a location or depth range in thewellbore 114, in general, any suitable monitoring system can be used.For example, various point sensors, thermocouples, resistive temperaturesensors, microphones, geophones, hydrophones, or other sensors can beused to provide temperature or acoustic measurements at a given locationbased on the temperature and/or acoustic measurement processingdescribed herein. Further, an optical fiber comprising a plurality ofpoint sensors such as Bragg gratings can also be used. As describedherein, a benefit of the use of the DTS system is that temperaturemeasurements can be obtained across a plurality of locations and/oracross a continuous length of the wellbore 114 rather than at discretelocations.

The monitoring system 110 can be used to obtain temperature measurementsand/or acoustic measurements along the length of the wellbore (or someportion thereof). The resulting measurements can be processed to obtainvarious temperature and/or acoustic based features that can then be usedto identify fluid outflow or inflow locations, identify outflow orinflowing fluid types, and/or quantify the rate of fluid outflow orinflow, etc. Each of the specific types of features obtained from themonitoring system are described in more detail below.

Referring still to FIG. 1 , fluid can be produced into the wellbore 114and into the completion assembly string (e.g., tubular 120). Duringoperations, the fluid flowing into the wellbore may comprise hydrocarbonfluids, such as, for instance hydrocarbon liquids (e.g., oil), gases(e.g., natural gas such as methane, ethane, etc.), and/or water, any ofwhich can also comprise particulates such as sand. However, the fluidflowing into the tubular may also comprise other components, such as,for instance steam, carbon dioxide, and/or various multiphase mixedflows. The fluid flow can further be time varying such as includingslugging, bubbling, or time altering flow rates of different phases. Theamounts or flow rates of these components can vary over time based onconditions within the formation 102 and the wellbore 114. Likewise, thecomposition of the fluid flowing into the tubular 120 sectionsthroughout the length of the entire production string (e.g., includingthe amount of sand contained within the fluid flow) can varysignificantly from section to section at any given time.

Conversely, during operations, fluid can be injected from wellbore 114into formation 102 during operations. For instance, water, steam, acid,slurry, or other suitable fluids or fluid combinations may be injectedinto formation 102 via wellbore 114 so as to increase a pressure of theformation 102 for driving or enhancing production in another wellbore(not shown in FIG. 1 ) extending within formation 102, to decrease aviscosity of hydrocarbons trapped within the formation 102 (e.g., suchas in steam assisted gravity drainage—SAGD—production systems), toincrease a porosity and/or permeability of the formation 102, etc.

As the fluid enters or exits the wellbore 114 (or some portion thereofsuch as, for instance tubular 120), the fluid can create acousticsignals and temperature changes that can be detected by the monitoringsystem 110 (e.g., via operations of the monitoring system 110 as a DTSsystem and/or a DAS systems as described herein). With respect to thetemperature variations, the temperature changes can result from variousfluid effects within the wellbore such as cooling based on gas enteringthe wellbore, temperature changes resulting from liquids entering orexiting the wellbore, changes in temperature within the zones (e.g.,zones 104 a, 104 b) based on fluid injected therein from wellbore 114,and various flow related temperature changes as a result of the fluidspassing through the wellbore. For example, as fluids enter the wellbore,the fluids can experience a sudden pressure drop, which can result in achange in the temperature. The magnitude of the temperature changedepends on the phase and composition of the inflowing fluid, thepressure drop, and the pressure and temperature conditions. The othermajor thermodynamic process that takes place as the fluid enters thewell is thermal mixing which results from the heat exchange between thefluid body that flows into the wellbore and the fluid that is alreadyflowing in the wellbore. As a result, inflow of fluids from thereservoir into the wellbore can cause a deviation in the flowing welltemperature profile.

In addition, the outflow or inflow of fluids (e.g., hydrocarbon liquids,hydrocarbon gas, water, etc.) into the wellbore 114 can also createdetectable acoustic sounds. Specifically, as fluid enters or exits flowports or openings in the tubular 120, vibrations are induced that cancreate acoustic signals within wellbore 114. Accordingly, the flow ofthe various fluids into the wellbore 114 and/or through the wellbore 114can create vibrations or acoustic sounds that can be detected usingacoustic monitoring system. Each type of fluid outflow or inflow (e.g.,such as the different fluid types, flow rates, and fluid flow locations)can produce a unique acoustic signature.

Referring still to FIG. 1 , when operating monitoring system 110 as aDTS system, a number of temperature features can be obtained from thetemperature measurements. The temperature features can provide anindication of one or more temperature trends at a given location in thewellbore during a measurement period. The resulting features can form adistribution of temperature results that can then be used with variousmodels to identify one or more events within the wellbore at thelocation.

The temperature measurements can represent output values from the DTSsystem, which can be used with or without various types ofpre-processing such as noise reduction, smoothing, and the like. Whenbackground temperature measurements are used, the background measurementcan represent a temperature measurement at a location within thewellbore taken in the absence of the flow of a fluid. For example, atemperature profile along the wellbore 114 can be taken when the well isinitially formed and/or the wellbore 114 can be shut in and allowed toequilibrate to some degree before measuring the temperatures at variouspoints in the wellbore. The resulting background temperaturemeasurements or temperature profile can then be used in determining thetemperature features in some embodiments.

In general, the temperature features represent statistical variations ofthe temperature measurements through time and/or depth. For example, thetemperature features can represent statistical measurements or functionsof the temperature within the wellbore that can be used with variousmodels to determine whether or not fluid inflow events have occurred.The temperature features can be determined using various functions andtransformations, and in some embodiments can represent a distribution ofresults. In some embodiments, the temperature features can represent anormal or Gaussian distribution. The resulting distributions can then beused with models such as multivariate models to determine the presenceof the fluid inflow events.

In some embodiments, the temperature features can include variousfeatures including, but not limited to, a depth derivative oftemperature with respect to depth, a temperature excursion measurement,a baseline temperature excursion, a peak-to-peak value, a Fast Fouriertransform (FFT), a Laplace transform, a wavelet transform, a derivativeof temperature with respect to depth, a heat loss parameter, anautocorrelation, and combinations thereof.

In some embodiments, the temperature features can comprise a depthderivative of temperature with respect to depth. This feature can bedetermined by taking the temperature measurements along the wellbore andsmoothing the measurements. Smoothing can comprise a variety of stepsincluding filtering the results, de-noising the results, or the like. Insome embodiments, the temperature measurements can be median filteredwithin a given window to smooth the measurements. Once smoothed, thechange in the temperature with depth can be determined. In someembodiments, this can include taking a derivative of the temperaturemeasurements with respect to depth along the longitudinal axis of thewellbore 114. The depth derivative of temperature values can then beprocessed, and the measurement with a zero value (e.g., representing apoint of no change in temperature with depth) that have preceding andproceeding values that are non-zero and have opposite signs in depth(e.g., zero below which the value is negative and above positive or viceversa) can have the values assign to the nearest value. This can thenresult in a set of measurements representing the depth derivative oftemperature with respect to depth.

In some embodiments, the temperature features can comprise a temperatureexcursion measurement. The temperature excursion measurement cancomprise a difference between a temperature reading at a first depth anda smoothed temperature reading over a depth range, where the first depthis within the depth range. In some embodiments, the temperatureexcursion measurement can represent a difference between de-trendedtemperature measurements over an interval and the actual temperaturemeasurements within the interval. For example, a depth range can beselected within the wellbore 114. The temperature readings within a timewindow can be obtained within the depth range and de-trended orsmoothed. In some embodiments, the de-trending or smoothing can includeany of those processes described above, such as using median filteringof the data within a window within the depth range. For medianfiltering, the larger the window of values used, the greater thesmoothing effect can be on the measurements. For the temperatureexcursion measurement, a range of windows from about 10 to about 100values, or between about 20-60 values (e.g., measurements of temperaturewithin the depth range) can be used to median filter the temperaturemeasurements. A difference can then be taken between the temperaturemeasurement at a location and the de-trended (e.g., median filtered)temperature values. The temperature measurements at a location can bewithin the depth range and the values being used for the medianfiltering. This temperature feature then represents a temperatureexcursion at a location along the wellbore 114 from a smoothedtemperature measurement over a larger range of depths around thelocation in the wellbore 114.

In some embodiments, the temperature features can comprise a baselinetemperature excursion. The baseline temperature excursion represents adifference between a de-trended baseline temperature profile and thecurrent temperature at a given depth. In some embodiments, the baselinetemperature excursion can rely on a baseline temperature profile thatcan contain or define the baseline temperatures along the length of thewellbore 114. As described herein, the baseline temperatures representthe temperature as measured when the wellbore 114 is shut in. This canrepresent a temperature profile of the formation in the absence of fluidflow. While the wellbore 114 may affect the baseline temperaturereadings, the baseline temperature profile can approximate a formationtemperature profile. The baseline temperature profile can be determinedwhen the wellbore 114 is shut in and/or during formation of the wellbore114, and the resulting baseline temperature profile can be used overtime. If the condition of the wellbore 114 changes over time, thewellbore 114 can be shut in and a new baseline temperature profile canbe measured or determined. It is not expected that the baselinetemperature profile is re-determined at specific intervals, and ratherit would be determined at discrete times in the life of the wellbore114. In some embodiments, the baseline temperature profile can bere-determined and used to determine one or more temperature featuressuch as the baseline temperature excursion.

Once the baseline temperature profile is obtained, the baselinetemperature measurements at a location in the wellbore 114 can besubtracted from the temperature measurement detected by the temperaturemonitoring system 110 at that location to provide baseline subtractedvalues. The results can then be obtained and smoothed or de-trended. Forexample, the resulting baseline subtracted values can be median filteredwithin a window to smooth the data. In some embodiments, a windowbetween 10 and 500 temperature values, between 50 and 400 temperaturevalues, or between 100 and 300 temperature values can be used to medianfilter the resulting baseline subtracted values. The resulting smoothedbaseline subtracted values can then be processed to determine a changein the smoothed baseline subtracted values with depth. In someembodiments, this can include taking a derivative of the smoothedbaseline subtracted values with respect to depth along the longitudinalaxis of the wellbore. The resulting values can represent the baselinetemperature excursion feature.

In some embodiments, the temperature features can comprise apeak-to-peak temperature value. This feature can represent thedifference between the maximum and minimum values (e.g., the range,etc.) within the temperature profile along the wellbore 114. In someembodiments, the peak-to-peak temperature values can be determined bydetecting the maximum temperature readings (e.g., the peaks) and theminimum temperature values (e.g., the dips) within the temperatureprofile along the wellbore 114. The difference can then be determinedwithin the temperature profile to determine peak-to-peak values alongthe length of the wellbore 114. The resulting peak-to-peak values canthen be processed to determine a change in the peak-to-peak values withrespect to depth. In some embodiments, this can include taking aderivative of the peak-to-peak values with respect to depth along thelongitudinal axis of the wellbore 114. The resulting values canrepresent the peak-to-peak temperature values.

Other temperature features can also be determined from the temperaturemeasurements. In some embodiments, various statistical measurements canbe obtained from the temperature measurements along the wellbore 114 todetermine one or more temperature features. For example, across-correlation of the temperature measurements with respect to timecan be used to determine a cross-correlated temperature feature. Thetemperature measurements can be smoothed as described herein prior todetermining the cross-correlation with respect to time. As anotherexample, an autocorrelation measurement of the temperature measurementscan be obtained with respect to depth. Autocorrelation is defined as thecross-correlation of a signal with itself. An autocorrelationtemperature feature can thus measure the similarity of the signal withitself as a function of the displacement. An autocorrelation temperaturefeature can be used, in applications, as a means of anomaly detectionfor event (e.g., fluid inflow) detection. The temperature measurementscan be smoothed and/or the resulting autocorrelation measurements can besmoothed as described herein to determine the autocorrelationtemperature features.

In some embodiments, the temperature features can comprise a FastFourier transform (FFT) of the distributed temperature sensing (e.g.,DTS) signal. This algorithm can transform the distributed temperaturesensing signal from the time domain into the frequency domain, thusallowing detection of the deviation in DTS along length (e.g., depth).This temperature feature can be utilized, for example, for anomalydetection for event (e.g., fluid inflow) detection purposes.

In some embodiments, the temperature features can comprise the Laplacetransform of DTS. This algorithm can transform the DTS signal from thetime domain into Laplace domain allows us to detect the deviation in theDTS along length (e.g., depth of wellbore 114). This temperature featurecan be utilized, for example, for anomaly detection for event (e.g.,fluid inflow) detection. This feature can be utilized, for example, inaddition to (e.g., in combination with) the FFT temperature feature.

In some embodiments, the temperature features can comprise a wavelettransform of the distributed temperature sensing (e.g., DTS) signaland/or of the derivative of DTS with respect to depth, dT/dz. Thewavelet transform can be used to represent the abrupt changes in thesignal data. This feature can be utilized, for example, in inflowdetection. A wavelet is described as an oscillation that has zero mean,which can thus make the derivative of DTS in depth more suitable forthis application. In embodiments and without limitation, the wavelet cancomprise a Morse wavelet, an Analytical wavelet, a Bump wavelet, or acombination thereof.

In some embodiments, the temperature features can comprise a derivativeof DTS with respect to depth, or dT/dz. The relationship between thederivative of flowing temperature T_(f) with respect to depth (L) (i.e.,dT_(f)/dL) has been described by several models. For example, andwithout limitation, the model described by Sagar (Sagar, R., Doty, D.R., & Schmidt, Z. (1991, November 1). Predicting Temperature Profiles ina Flowing Well. Society of Petroleum Engineers. doi:10.2118/19702-PA)which accounts for radial heat loss due to conduction and describes arelationship (Equation (1) below) between temperature change in depthand mass rate. The mass rate w_(t) is conversely proportional to therelaxation parameter A and, as the relaxation parameter A increases, thechange in temperature in depth increases. Hence this temperature featurecan be designed to be used, for example, in events comprising inflowquantification.

$\begin{matrix}{\frac{dT_{f}}{dL} = {- {{A\lbrack {( {T_{f} - T_{e}} ) + {\frac{g}{g_{c}}\frac{\sin\theta}{{JC}_{pm}A}} - \frac{F_{c}}{A}} \rbrack}.}}} & (1)\end{matrix}$

The formula for the relaxation parameter, A, is provided in Equation(2):

$\begin{matrix}{A = {( \frac{2\pi}{w_{t}C_{pl}} )( \frac{r_{R}{Uk}_{e}}{k_{e} + {r_{R}Uf/12}} )( \frac{I}{86,400 \times 12} )}} & (2)\end{matrix}$

-   -   A=coefficient, ft⁻¹    -   C_(pL)=specific beat of liquid, Btu/lbm-° F.    -   C_(pm)=specific heat of mixture, Btu/lbm-° F.    -   C_(po)=specific heat of oil, Btu/lbm-° F.    -   C_(pw)=specific heat of water, Btu/lbm-° F.    -   d_(c)=casing diameter, in.    -   d_(t)=tubing diameter, in.    -   d_(wb)=wellbore diameter, in.    -   D=depth, ft    -   D_(inj)=injection depth, ft    -   f=modified dimensionless heat conduction time function for long        times for earth    -   f(t)=dimensionless transient heat conduction time function for        earth    -   F_(c)=correction factor    -   F_(c) =average correction factor for one length interval    -   g=acceleration of gravity, 32.2 ft/sec²    -   g_(c)=conversion factor, 32.2 ft-lbm/sec²-lbf    -   g_(G)=geothermal gradient, ° F./ft    -   h=specific enthalpy, ° F./lbm    -   J=mechanical equivalent of ben, 778 ft-lbf/Btu    -   k_(an)=thermal conductivity of material in annulus, Btu/D-ft-°        F.    -   k_(ang)=thermal conductivity of gas in annulus, Btu/D-ft-° F.    -   k_(anw)=thermal conductivity of water in annulus, Btu/D-ft-° F.    -   k_(cem)=thermal conductivity of cement, Btu/D-ft-° F.    -   k_(e)=thermal conductivity of earth, Btu/D-ft-° F.    -   L=length of well from perforations, ft    -   L_(in)=length from perforation to inlet, ft    -   p=pressure, psi    -   p_(wh)=wellhead pressure, psig    -   q_(gf)=formation gas flow rate, scf/D    -   q_(ginj)=injection gas flow rate, scf/D    -   q_(o)=oil flow rate, STB/D    -   q_(w)=water flow rate, STB/D    -   Q=beat transfer between fluid and surrounding area, Btu/lbm    -   r_(ci)=inside casing radius, in.    -   r_(co)=outside casing radius, in.    -   r_(ti)=inside tubing radius, in.    -   r_(to)=outside tubing radius, in.    -   r_(wb)=wellbore radius, in.    -   R_(gL)=gas/liquid ratio, scf/STB    -   T=temperature, ° F.    -   T_(bh)=bottomhole temperature, ° F.    -   T_(c)=casing temperature, ° F.    -   T_(e)=surrounding earth temperature, ° F.    -   T_(ein)=earth temperature at inlet, ° F.    -   T_(f)=flowing fluid temperature, ° F.    -   T_(fin)=flowing fluid temperature at inlet, ° F.    -   T_(h)=cement/earth interface temperature, ° F.    -   U=overall heat transfer coefficient, Btu/D-ft²-° F.    -   v=fluid velocity, ft/sec    -   V=volume    -   w_(t)=total mass now rate, lbm/sec    -   Z=height from bottom of hole, ft    -   Z_(in)=height from bottom of hole at inlet, ft    -   α=thermal diffusivity of earth, 0.04 ft²/hr    -   γ_(API)=oil gravity, ° API    -   γ_(g)=gas specific gravity (air=1)    -   γ_(o)=oil specific gravity    -   γ_(w)=water specific gravity    -   θ=angle of inclination, degrees    -   μ=Joule-Thomson coefficient

In some embodiments, the temperature features can comprise a heat lossparameter. As described hereinabove, Sagar's model describes therelationship between various input parameters, including the mass ratew_(t) and temperature change in depth dT_(f)/dL. These parameters can beutilized as temperature features in a machine learning model which usesfeatures from known cases (production logging results) as learning datasets, when available. These features can include geothermal temperature,deviation, dimensions of the tubulars 120 that are in the well (casing112, tubing 120, gravel pack 122 components, etc.), as well as thewellbore 114, well head pressure, individual separator rates, downholepressure, gas/liquid ratio, and/or a combination thereof. Such heat lossparameters can, for example, be utilized as inputs in a machine learningmodel for events comprising inflow quantification of the mass flow ratew_(t).

In some embodiments, the temperature features can be based on dynamictemperature measurements rather than steady state or flowing temperaturemeasurements. In order to obtain dynamic temperature measurements, achange in the operation of the wellbore 114 can be introduced, and thetemperature monitored using the temperature monitoring system. Thechange in conditions can be introduced by shutting in the wellbore 114,opening one or more sections of the wellbore 114 to flow, introducing afluid to the wellbore 114 (e.g., injecting a fluid), and the like. Whenthe wellbore 114 is shut in from a flowing state, the temperatureprofile along the wellbore 114 may be expected to change from theflowing profile to the baseline profile over time. Similarly, when awellbore 114 that is shut in is opened for flow, the temperature profilemay change from a baseline profile to a flowing profile. Based on thechange in the condition of the wellbore 114, the temperaturemeasurements can change dynamically over time. In some embodiments, thisapproach can allow for a contrast in thermal conductivity to bedetermined between a location or interval having radial flow (e.g., intoor out of the wellbore) to a location or interval without radial flow.One or more temperature features can then be determined using thedynamic temperature measurements. Once the temperature features aredetermined from the temperature measurements obtained from thetemperature monitoring system, one or more of the temperature featurescan be used to identify events (e.g., fluid inflow events within awellbore), as described in more detail herein.

Any of these temperature features, or any combination of thesetemperature features (including transformations of any of thetemperature features and combinations thereof), can be used to detectone or more events. In an embodiment, a selected set of characteristicscan be used to identify the presence or absence for each event, and/orall of the temperature features that are calculated can be used as agroup in characterizing the presence or absence of an event. Thespecific values for the temperature features that are calculated canvary depending on the specific attributes of the temperature signalacquisition system, such that the absolute value of each temperaturefeature can change between systems. In some embodiments, the temperaturefeatures can be calculated for each event based on the system being usedto capture the temperature signal and/or the differences between systemscan be taken into account in determining the temperature feature valuesfor each event between or among the systems used to determine the valuesand the systems used to capture the temperature signal being evaluated.

One or a plurality of temperature features can be used to identifyevents. In an embodiment, one, or at least two, three, four, five, six,seven, eight, etc. different temperature features can be used to detectevents. The temperature features can be combined or transformed in orderto define the event signatures for one or more events, such as, forinstance, a fluid inflow event location or flowrate. The actualnumerical results for any temperature feature may vary depending on thedata acquisition system and/or the values can be normalized or otherwiseprocessed to provide different results.

Referring still to FIG. 1 , when operating monitoring system 110 as aDAS system, a number of frequency domain features can be determined forthe acoustic sample data. However, not every frequency domain featuremay be used in the identifying fluid flow characteristics, inflow oroutflow locations, flow type, or flow rate classification or prediction.The frequency domain features represent specific properties orcharacteristics of the acoustic signals. There are a number of factorsthat can affect the frequency domain feature selection for each fluidinflow event. For example, a chosen descriptor should remain relativelyunaffected by the interfering influences from the environment such asinterfering noise from the electronics/optics, concurrent acousticsounds, distortions in the transmission channel, and the like. Ingeneral, electronic/instrumentation noise is present in the acousticsignals captured on the DAS or any other electronic gauge, and it isusually an unwanted component that interferes with the signal. Thermalnoise is introduced during capturing and processing of signals byanalogue devices that form a part of the instrumentation (e.g.,electronic amplifiers and other analog circuitry). This is primarily dueto thermal motion of charge carriers. In digital systems additionalnoise may be introduced through sampling and quantization. The frequencydomain features should have values that are significant for a givenevent in the presence of noise.

As a further consideration in selecting the frequency domain feature(s)for a fluid outflow or inflow event in some embodiments, thedimensionality of the frequency domain feature should be compact. Acompact representation may be desired to decrease the computationalcomplexity of subsequent calculations. It may also be desirable for thefrequency domain feature to have discriminant power. For example, fordifferent types of audio signals, the selected set of descriptors shouldprovide altogether different values. A measure for the discriminantpower of a feature is the variance of the resulting feature vectors fora set of relevant input signals. Given different classes of similarsignals, a discriminatory descriptor should have low variance insideeach class and high variance over different classes. The frequencydomain feature should also be able to completely cover the range ofvalues of the property it describes.

In some embodiments, combinations of frequency domain features can beused. This can include a signature having multiple frequency domainfeatures as indicators. In some embodiments, a plurality of frequencydomain features can be transformed to create values that can be used todefine various event signatures. This can include mathematicaltransformations including ratios, equations, rates of change, transforms(e.g., wavelets, Fourier transforms, other wave form transforms, etc.),other features derived from the feature set, and/or the like as well asthe use of various equations that can define lines, surfaces, volumes,or multi-variable envelopes. The transformation can use othermeasurements or values outside of the frequency domain features as partof the transformation. For example, time domain features, other acousticfeatures, and non-acoustic measurements can also be used. In this typeof analysis, time can also be considered as a factor in addition to thefrequency domain features themselves. As an example, a plurality offrequency domain features can be used to define a surface (e.g., aplane, a three-dimensional surface, etc.) in a multivariable space, andthe measured frequency domain features can then be used to determine ifthe specific readings from an acoustic sample fall above or below thesurface. The positioning of the readings relative to the surface canthen be used to determine if the event if present or not at thatlocation in that detected acoustic sample.

As an example, the chosen set of frequency domain features should beable to uniquely identify the event signatures with a reasonable degreeof certainty of each of the acoustic signals pertaining to a selecteddownhole surveillance application or fluid outflow or inflow event asdescribed herein. Such frequency domain features can include, but arenot limited to, the spectral centroid, the spectral spread, the spectralroll-off, the spectral skewness, the root mean square (RMS) band energy(or the normalized sub-band energies/band energy ratios), a loudness ortotal RMS energy, a spectral flatness, a spectral slope, a spectralkurtosis, a spectral flux, a spectral autocorrelation function, or anormalized variant thereof.

The spectral centroid denotes the “brightness” of the sound captured bythe optical fiber (e.g., optical fiber 162 shown in FIG. 1 ) andindicates the center of gravity of the frequency spectrum in theacoustic sample. The spectral centroid can be calculated as the weightedmean of the frequencies present in the signal, where the magnitudes ofthe frequencies present can be used as their weights in someembodiments.

The spectral spread is a measure of the shape of the spectrum and helpsmeasure how the spectrum is distributed around the spectral centroid. Inorder to compute the spectral spread, S_(i), one has to take thedeviation of the spectrum from the computed centroid as per thefollowing equation (all other terms defined above):

$\begin{matrix}{S_{i} = {\sqrt{\frac{{\Sigma_{k = 1}^{N}( {{f(k)} - C_{i}} )}^{2}{X_{i}(k)}}{\Sigma_{k = 1}^{N}{X_{i}(k)}}}.}} & (3)\end{matrix}$

The spectral roll-off is a measure of the bandwidth of the audio signal.The Spectral roll-off of the i^(th) frame, is defined as the frequencybin ‘y’ below which the accumulated magnitudes of the short-time Fouriertransform reach a certain percentage value (usually between 85%-95%) ofthe overall sum of magnitudes of the spectrum.

$\begin{matrix}{{{\sum_{k = 1}^{y}{❘{X_{i}(k)}❘}} = {\frac{c}{100}{\sum_{k = 1}^{N}{❘{X_{i}(k)}❘}}}},} & (4)\end{matrix}$

where c=85 or 95. The result of the spectral roll-off calculation is abin index and enables distinguishing acoustic events based on dominantenergy contributions in the frequency domain (e.g., between gas influxand liquid flow, etc.).

The spectral skewness measures the symmetry of the distribution of thespectral magnitude values around their arithmetic mean.

The RMS band energy provides a measure of the signal energy withindefined frequency bins that may then be used for signal amplitudepopulation. The selection of the bandwidths can be based on thecharacteristics of the captured acoustic signal. In some embodiments, asub-band energy ratio representing the ratio of the upper frequency inthe selected band to the lower frequency in the selected band can rangebetween about 1.5:1 to about 3:1. In some embodiments, the sub-bandenergy ratio can range from about 2.5:1 to about 1.8:1, or alternativelybe about 2:1. The total RMS energy of the acoustic waveform calculatedin the time domain can indicate the loudness of the acoustic signal. Insome embodiments, the total RMS energy can also be extracted from thetemporal domain after filtering the signal for noise.

The spectral flatness is a measure of the noisiness/tonality of anacoustic spectrum. It can be computed by the ratio of the geometric meanto the arithmetic mean of the energy spectrum value and may be used asan alternative approach to detect broad-banded signals. For tonalsignals, the spectral flatness can be close to 0 and for broader bandsignals it can be closer to 1.

The spectral slope provides a basic approximation of the spectrum shapeby a linearly regressed line. The spectral slope represents the decreaseof the spectral amplitudes from low to high frequencies (e.g., aspectral tilt). The slope, the y-intersection, and the max and mediaregression error may be used as features.

The spectral kurtosis provides a measure of the flatness of adistribution around the mean value.

The spectral flux is a measure of instantaneous changes in the magnitudeof a spectrum. It provides a measure of the frame-to-frame squareddifference of the spectral magnitude vector summed across allfrequencies or a selected portion of the spectrum. Signals with slowlyvarying (or nearly constant) spectral properties (e.g., noise) have alow spectral flux, while signals with abrupt spectral changes have ahigh spectral flux. The spectral flux can allow for a direct measure ofthe local spectral rate of change and consequently serves as an eventdetection scheme that could be used to pick up the onset of acousticevents that may then be further analyzed using the feature set above toidentify and uniquely classify the acoustic signal.

The spectral autocorrelation function provides a method in which thesignal is shifted, and for each signal shift (lag) the correlation orthe resemblance of the shifted signal with the original one is computed.This enables computation of the fundamental period by choosing the lag,for which the signal best resembles itself, for example, where theautocorrelation is maximized. This can be useful in exploratorysignature analysis/even for anomaly detection for well integritymonitoring across specific depths where well barrier elements to bemonitored are positioned.

Any of these frequency domain features, or any combination of thesefrequency domain features (including transformations of any of thefrequency domain features and combinations thereof), can be used todetermine the location, type, and flow rate of fluid inflow or the fluidinflow discrimination as described hereinbelow. In an embodiment, aselected set of characteristics can be used to identify the presence orabsence for each fluid outflow or inflow event, and/or all of thefrequency domain features that are calculated can be used as a group incharacterizing the presence or absence of a fluid outflow or inflowevent. The specific values for the frequency domain features that arecalculated can vary depending on the specific attributes of the acousticsignal acquisition system, such that the absolute value of eachfrequency domain feature can change between systems. In someembodiments, the frequency domain features can be calculated for eachevent based on the system being used to capture the acoustic signaland/or the differences between systems can be taken into account indetermining the frequency domain feature values for each fluid inflowevent between or among the systems used to determine the values and thesystems used to capture the acoustic signal being evaluated.

One or a plurality of frequency domain features can be used tocharacterize each type of event (e.g., fluid outflow, fluid inflow,etc.) and/or to classify the flow rate of each identified type of fluidoutflow/inflow (e.g., water, gas, hydrocarbon liquid, etc.). In anembodiment, one, or at least two, three, four, five, six, seven, eight,etc. different frequency domain features can be used to characterizeeach type of event and/or to classify the flow rate of each identifiedtype of fluid flow/inflow. The frequency domain features can be combinedor transformed in order to define the event signatures for one or moreevents. While exemplary numerical ranges are provided herein, the actualnumerical results may vary depending on the data acquisition systemand/or the values can be normalized or otherwise processed to providedifferent results.

Referring now to FIG. 3 , a well system 180 is shown. Well system 180includes a plurality of wellbores 114A, 114B extending into subterraneanformation 102. In the depiction of FIG. 3 , the well system 180 includestwo wellbores 114A, 114B; however, the number and arrangement of theplurality of wellbores within embodiments of well system 180 may bevaried in different embodiments. Each of the wellbores 114A, 114B may begenerally configured the same or similar to the wellbore 114 of FIG. 1 ,previously described above. Thus, structural details of the wellbores114A, 114B are omitted in FIG. 3 so as to simplify the drawing, and itshould be appreciated that the description above for wellbore 114 may beapplied to describe various embodiments of wellbores 114A, 114B.

Wellbores 114A, 114B may each comprise a corresponding monitoring system110A, 110B, respectively. Monitoring systems 110A, 110B may be generallythe same as monitoring system 110 shown in FIG. 1 and previouslydescribed above. Thus, many of the details of the monitoring system 110are omitted so as to simplify the drawing, and it should be appreciatedthat the description above for monitoring system 110 may be applied todescribe various embodiments of monitoring systems 110A, 110B.Accordingly, monitoring systems 110A, 110B may be configured to as DTSsystems, DAS systems, or both as previously described. As shown in FIG.3 , each monitoring system 110A, 110B includes a corresponding opticalfiber 162A, 162B, respectively, and acquisition device 160A, 160B,respectively (which may generally be the same as the optical fiber 162and acquisition device 160 shown in FIG. 1 and previously describedabove).

During operations, fluids may be injected into formation 102 via one orboth of the wellbores 114A, 114B, and fluids may be produced fromformation 102 into one or both of the wellbores 114A, 114B. During theseoperations, monitoring systems 110A, 110B may be utilized tocharacterize the fluid flows into, out of, and between the wellbores110A, 110B. Specifically, the monitoring systems 110A, 110B (e.g., viaoptical fibers 162A, 162B, and acquisition systems 160A, 160B,respectively) may capture distributed temperature and/or acousticsignals within wellbores 114A, 114B, and via various analysis methods asdescribed herein may monitor, identify, and characterize various aspectsof the fluids flows into, out of, and/or between wellbores 114A, 114B.

In some specific examples, a fluid (e.g., water) may be injected intoformation 102 via wellbore 114A. The injected fluid may then flow intoformation toward second wellbore 114B, and wellbore 114B may receiveformation fluids (e.g., hydrocarbon liquids, hydrocarbon gases, nativeformation water, etc.), injected fluid (e.g., from wellbore 114A, orboth, which may then be produced, via wellbore 114B, to the surface.During these operations, the monitoring systems 110A, 110B, viaembodiments of one or more of the methods described herein, may monitorand characterize the fluid flow out of wellbore 114A, the fluid flowinto wellbore 114B, and/or the fluid flow within the formation 102(e.g., between wellbores 114A, 114B).

Referring now to FIG. 4 , a flow chart of a method 200 of characterizinga fluid flow into and/or out of a wellbore extending within asurrounding subterranean formation according to some embodiments of thisdisclosure is shown. Generally speaking, method 200 may be utilized tocharacterize fluid flow out of and/or into a wellbore with a DAS system(e.g., such as monitoring system 110 of FIG. 1 ). Without being limitedto this or any other theory, by characterizing the various flows intoand/or out of a wellbore, a well operator may have a more completeunderstanding of the status, health, and condition of the wellboreduring operations

Initially, method 200 includes obtaining an acoustic signal at 202. Suchan acoustic signal can be obtained via any suitable method or system.For instance, in some embodiments, the acoustic signal at block 202 maybe obtained utilizing a DAS system (e.g., monitoring systems 110, 110A,110B, previously described above) installed at least partially within awellbore (e.g., wellbore 114, 114A, 114B, etc.). In some embodiments,the acoustic signal obtained at 202 may include vibrations that resultedfrom the flow of fluid into or out of the wellbore. In some embodiments,the acoustic signals obtained at 202 can include frequencies in therange of about 5 Hz to about 10 kHz, frequencies in the range of about 5Hz to about 5 kHz or about 50 Hz to about 5 kHz, or frequencies in therange of about 500 Hz to about 5 kHz. Any frequency ranges between thelower frequencies values (e.g., 5 Hz, 50 Hz, 500 Hz, etc.) and the upperfrequency values (e.g., 10 kHz, 7 kHz, 5 kHz, etc.) can be used todefine the frequency range for a broadband acoustic signal.

Referring again to FIG. 4 , after the acoustic signal is obtained at202, method 200 may proceed, in some embodiments, to pre-process the rawdata at 204. The acoustic signal can be generated within a wellbore aspreviously described. Depending on the type of DAS system employed(e.g., monitoring system 110 in FIG. 1 ), the optical data of theacoustic signal may or may not be phase coherent and may be preprocessedto improve the signal quality (e.g., denoised for opto-electronic noisenormalization/de-trending single point-reflection noise removal throughthe use of median filtering techniques or even through the use ofspatial moving average computations with averaging windows set to thespatial resolution of the acquisition unit, etc.). The raw optical datafrom the acoustic sensor (e.g., optical fiber 162, 162A, 162B, etc.) canbe received, processed, and generated by the sensor to produce theacoustic signal. The data rate generated by various acoustic sensorssuch as a DAS system (e.g., monitoring system 110) can be large. Forexample, the DAS system may generate data on the order of 0.5 to about 2terabytes per hour. This raw data can optionally be stored in a memory(e.g., memory 170 for monitoring system 110 in FIG. 1 ).

A number of specific processing steps can be performed to determine thelocation of fluid outflow, the presence and location of fluid inflow,the composition of inflowing fluid, and/or the flow rate or volume ofthe outflowing or inflowing fluid (see e.g., blocks 210, 214, describedin more detail below). In some embodiments, a processor or collection ofprocessors (e.g., processor 168 in FIG. 1 ) may be utilized to performthe preprocessing steps described herein. In an embodiment, the noisedetrended “acoustic variant” data can be subjected to an optionalspatial filtering step following the other preprocessing steps, ifpresent. A spatial sample point filter can be applied that uses a filterto obtain a portion of the acoustic signal corresponding to a desireddepth or depth range in the wellbore. Since the time the light pulsesent into the optical fiber returns as backscattered light cancorrespond to the travel distance, and therefore depth in the wellbore,the acoustic data can be processed to obtain a sample indicative of thedesired depth or depth range. This may allow a specific location withinthe wellbore to be isolated for further analysis. The preprocessing at204 may also include removal of spurious back reflection type noises atspecific depths through spatial median filtering or spatial averagingtechniques. This is an optional step and helps focus primarily on aninterval of interest in the wellbore. For example, the spatial filteringstep can be used to focus on a producing interval where there is maximumlikelihood of fluid inflow, for example. The resulting data set producedthrough the conversion of the raw optical data can be referred to as theacoustic sample data.

Filtering can provide several advantages. For instance, when theacoustic data set is spatially filtered, the resulting data, for examplethe acoustic sample data, used for the next step of the analysis can beindicative of an acoustic sample over a defined depth (e.g., the entirelength of the optical fiber, some portion thereof, or a point source inthe wellbore 114). In some embodiments, the acoustic data set cancomprise a plurality of acoustic samples resulting from the spatialfilter to provide data over a number of depth ranges. In someembodiments, the acoustic sample may contain acoustic data over a depthrange sufficient to capture multiple points of interest. In someembodiments, the acoustic sample data contains information over theentire frequency range of the detected acoustic signal at the depthrepresented by the sample. This is to say that the various filteringsteps, including the spatial filtering, do not remove the frequencyinformation from the acoustic sample data.

In some embodiments, the filtered data may be additionally transformedfrom the time domain into the frequency domain using a transform at 204(e.g., after it has been filtered—such as spatially filtered asdescribed above). For example, Discrete Fourier transformations (DFT) ora short time Fourier transform (STFT) of the acoustic variant timedomain data measured at each depth section along the fiber or a sectionthereof may be performed to provide the data from which the plurality offrequency domain features can be determined. The frequency domainfeatures can then be determined from the acoustic data. Spectral featureextraction using the frequency domain features through time and spacecan be used to determine the spectral conformance (e.g., whether or notone or more frequency domain features match or conform to certainsignature thresholds) and determine if an acoustic signature (e.g., afluid inflow signature, a gas phase inflow signature, a water phaseinflow signature, a hydrocarbon liquid phase inflow signature, etc.) ispresent in the acoustic sample. Within this process, various frequencydomain features can be calculated for the acoustic sample data.

Preprocessing at 204 can optionally include a noise normalizationroutine to improve the signal quality. This step can vary depending onthe type of acquisition device used as well as the configuration of thelight source, the sensor, and the other processing routines. The orderof the aforementioned preprocessing steps can be varied, and any orderof the steps can be used.

Preprocessing at 204 can further comprise calibrating the acousticsignal. Calibrating the acoustic signal can comprise removing abackground signal from the acoustic signal, aligning the acoustic datawith physical depths in the wellbore, and/or correcting the acousticsignal for signal variations in the measured data. The background signalmay comprise background noise that is generated by the flowing of fluidswithin the wellbore, and/or vibrations that are not associated with thefluid inflows or outflows of interest. In some embodiments, calibratingthe acoustic signal comprises identifying one or more anomalies withinthe acoustic signal and removing one or more portions of the acousticsignal outside the one or more anomalies.

Following the preprocessing at 204, method 200 may determine one or morefrequency domain features from the acoustic signal at 206. As usedherein, “one or more” expressly includes “one,” or “a plurality of.”Thus, “one or more” frequency domain features may include one frequencydomain feature or a plurality of frequency domain features. The use offrequency domain features to identify outflow locations, inflowlocations, inflow type discrimination, and outflow or inflow flow rateor volume can provide a number of advantages. First, the use offrequency domain features results in significant data reduction relativeto the raw DAS data stream. Thus, a number of frequency domain featurescan be calculated and used to allow for event identification while theremaining data can be discarded or otherwise stored, and the remaininganalysis can performed using the frequency domain features. Even whenthe raw DAS data is stored, the remaining processing power issignificantly reduced through the use of the frequency domain featuresrather than the raw acoustic data itself. Further, the use of thefrequency domain features can, with the appropriate selection of one ormore of the frequency domain features, provide a concise, quantitativemeasure of the spectral character or acoustic signature of specificsounds pertinent to downhole fluid surveillance and other applications.The frequency domain features obtained at block 206 may comprise one ormore of the frequency domain features described herein includingcombinations, variants (e.g., a normalized variant), and/ortransformations thereof.

Referring still to FIG. 4 , as previously described in some embodimentsmethod 200 may also comprise normalizing the one or more frequencydomain features at 208. Any suitable normalization procedure and/oralgorithm may be employed at block 208. As a result, a detailedexplanation of this step is not included herein in the interests ofbrevity. In some embodiments, block 208 omitted from method 200.

Following block 206 (and potentially the normalization at block 208 aspreviously described above), method 200 may then progress to block 210and/or block 218. In some embodiments, method 200 may progress to block210 and not block 218 (or block 218 and not block 210) following block206 (or block 208). In some embodiments, method 200 may progress to bothblock 210 and block 218 (e.g., simultaneously, consecutively, etc.).Thus, it should be understood that in some embodiments of method 200,one of the blocks 210, 218 (and blocks that proceed from or rely uponblocks 210, 218) may not be performed.

Referring still to FIG. 4 , block 210 may comprise identifying at leastone fluid outflow location within the wellbore using the one or morefrequency domain features. In some embodiments, the one or morefrequency domain features utilized at block 210 may comprise one or moreof the frequency domain features described herein includingcombinations, variants (e.g., a normalized variant), and/ortransformations thereof. For instance, in some embodiments, at least twosuch frequency domain features (and/or combinations, variants, ortransformations thereof) are utilized at block 210. In some embodiments,the frequency domain features utilized within block 210 may comprise aratio between at least two of the plurality of the frequency domainfeatures. Specifically, in some embodiments, the frequency domainfeatures utilized at 210 may comprise a normalized variant of thespectral spread and/or a normalized variant of the spectral centroid. Insome embodiments, identifying the one or more fluid outflow locationscomprises identifying one or more anomalies in the acoustic signal usingthe one or more frequency domain features of the plurality of frequencydomain features; and selecting the depth intervals of the one or moreanomalies as including or being the one or more outflow locations.

Referring briefly again to FIG. 1 , in some embodiments, the one or moreoutflow locations may comprise locations where fluid (e.g., water,glycol, acid, other suitable injection fluids, etc.) is flowing from atubular within the wellbore into an annular space between the tubularand the borehole wall. For instance, for the wellbore 114 of FIG. 1 ,the one or more outflow locations may comprise locations where fluid isflowing from tubular 120 into the annulus 119. As previously described,the annular space 119 may be separated into a plurality of zones orintervals by the plurality of zonal isolation devices 117 (e.g.,packers). Thus, in some embodiments the one or more outflow locationsmay not comprise precise locations where fluid is entering formation102, but may indicate a general depth interval, between zonal isolationdevices 117, where fluid is exiting tubular 120 and is thereby exposedto the wall of the formation 102. Within each depth interval, fluid mayenter the formation 102 at one or a plurality of points or locations(e.g., such as at one or more different perforations, cracks, or otherentrance points/locations into the formation). As described in moredetail below, these subsequent points or areas of entry into theformation may be referred to as “fluid uptake locations.”

In some embodiments, block 210 of method 10 may comprise providing theone or more frequency domain features to a fluid outflow model (e.g., alogistic regression model) at 212 and determining that at least onefluid outflow is present within the wellbore (or along a length or depthrange of interest), based on the fluid flow model. In some embodiments,the fluid outflow model can be developed using and/or may includemachine learning such as a neural network, a Bayesian network, adecision tree, a logistical regression model, or a normalized logisticalregression, or other supervised learning models. In some embodiments,the model at 212 may define a relationship between at least two of theplurality of the frequency domain features, including in someembodiments combinations, variations, and/or transformations of thefrequency domain features and one or more fluid flows. In someembodiments, block 212 may comprise utilizing a plurality of differentmodels to identify the one or more fluid outflow locations within thewellbore (e.g., wellbore 114 in FIG. 1 ). In some embodiments, one ormore of the plurality of models may comprise multivariable models. Insome of these embodiments, the plurality of models may utilize one ormore of the frequency domain features (which may or may not be the samein each model) as inputs therein. In some embodiments, the plurality ofmodels may utilize at least two of the frequency domain features asinputs therein.

Once the one or more fluid outflow locations are determined via blocks210, 212, method 200 may next include determining an allocation of atotal injected fluid flow across the one or more fluid outflow locationsusing the one or more frequency domain features at block 214. In someembodiments, a total injected fluid flow comprises a total flow rate(e.g., volumetric flow rate) that is injected into the wellbore (e.g.,wellbore 114) from the surface. This total volumetric flow rate may beknown based on one or more flow meters within or upstream of wellbore114, and that may be separate from the DAS system (e.g., monitoringsystem 110).

The allocation of the total volumetric flow rate may be determined byinputting one or more or a plurality of the frequency domain featuresdetermined at block 206 into an additional fluid flow model. In someembodiments, the additional fluid flow model may be the same or similarto one or more of the fluid outflow model(s) utilized at blocks 210, 212as previously described. Thus, in some embodiments, blocks 210, 212, and214 may be merged so as to determine both identify the one or moreoutflow locations as well as to determine the allocation of the totalinjected fluid flow across the one or more outflow locations in a singleblock. In some embodiments, the fluid model(s) (or at least somethereof) for determining an allocation of the total injected fluid flowmay be separate from the fluid outflow model(s) utilized at blocks 210,212. In various embodiments, the fluid flow model(s) utilized at block214 may use one or more of the frequency domain features in a similarmanner to that described above for the fluid outflow flow model(s) ofblock 212.

In some embodiments, the allocation may comprise classifying the flowrate into one or more flow rate buckets (e.g., low, medium, high, etc.)and then estimating an allocation of the total volumetric flow amongstthe one or more fluid outflow locations based on the classification. Forinstance, the flow rate of the outflowing fluid at the one or moreoutflow locations may be classified via the methods described in moredetail below for the inflowing fluid at one or more fluid inflowlocations at blocks 222. In some embodiments, the allocation maycomprise a comparison between select ones, groups, and/or combinationsof frequency domain features so as to compare the acoustic measurementsdetected at these locations and then determine, based on thiscomparison, a relative allocation of the total injected fluid flowtherebetween. For instance, generally speaking, an increased fluid flowrate through a fluid outflow location may be expected to increase theacoustic signal intensity (e.g., amplitude) at that fluid outflowlocation. Thus, a comparison between the intensities (either alone oralong with other values, such, frequency domain features) may allow foran estimate of an allocation of the total injected fluid flow out of thewellbore at across the one or more outflow locations at block 214.

Referring still to FIG. 4 , as previously described, in someembodiments, following determining the one or more frequency domainfeatures at block 206 or normalizing the one or more frequency domainfeatures at block 208, method 200 may progress to block 218 directly orvia block 216. Block 218 comprises identifying at least one of a gasphase inflow, an aqueous phase inflow, or a hydrocarbon liquid phaseinflow using the one or more or a plurality of frequency domain featuresat one or more fluid inflow locations. In some embodiments, method 200may include identifying the one or more fluid inflow locations at 216prior to progressing to block 218. As is also shown in FIG. 4 , in someembodiments, method 200 may proceed to identifying the one or more fluidinflow locations at 216 without first normalizing the frequency domainfeatures at 218.

The one or more fluid inflow locations at 216 may comprise locationsalong the wellbore where fluid (e.g., formation fluids) are flowing intothe wellbore or a tubular member thereof. For instance, for the wellbore114 of FIG. 1 , the one or more fluid inflow locations may compriselocations where fluid from the formation (e.g., hydrocarbon gas, water,hydrocarbon liquid, etc.) is flowing into the tubular 120.

At block 216, the one or more fluid inflow locations may be determinedvia other data, knowledge or experience known to those of havingordinary skill. For instance, in some embodiments, the one or more fluidinflow locations may be determined via PLS data at 216. In someembodiments, block 216 may comprise identifying the one or more fluidflow and/or inflow locations using one or more of the frequency domainfeatures to identify acoustic signals corresponding to the inflow, andcorrelating the depths of those signals with locations within thewellbore. The one or more frequency domain features can comprise atleast two different frequency domain features in some embodiments. Insome embodiments, the one or more frequency domain features utilized todetermine the one or more fluid inflow locations comprises at least oneof a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, an RMS band energy, a total RMS energy, a spectralflatness, a spectral slope, a spectral kurtosis, a spectral flux, aspectral autocorrelation function, as well as combinations,transformations, and/or normalized variant(s) thereof.

In some embodiments, block 216 of method 200 may comprise: identifying abackground fluid flow signature using the acoustic signal; and removingthe background fluid flow signature from the acoustic signal prior toidentifying the one or more fluid inflow locations. In some embodiments,identifying the one or more fluid inflow locations comprises identifyingone or more anomalies in the acoustic signal using the one or morefrequency domain features of the plurality of frequency domain features;and selecting the depth intervals of the one or more anomalies as theone or more inflow locations. When a portion of the signal is removed(e.g., a background fluid flow signature, etc.), the removed portion canalso be used as part of the event analysis. Thus, in some embodiments,identifying the one or more fluid inflow locations at block 216comprises: identifying a background fluid flow signature using theacoustic signal; and using the background fluid flow signature from theacoustic signal to identify the one or more fluid inflow locations.

In some embodiments, method 200 may progress to block 218 followingblock 216 or following blocks 218 and/or 216 as previously describedabove and shown in FIG. 4 . In some embodiments, the one or morefrequency domain features utilized at block 218 may comprise frequencydomain features described herein including combinations, variants (e.g.,a normalized variant), and/or transformations thereof. For instance, insome embodiments, at least two such frequency domain features (and/orcombinations, variants, or transformations thereof) are utilized atblock 218. In some embodiments, the frequency domain features utilizedwithin block 218 may comprise a ratio between at least two of theplurality of the frequency domain features. Specifically, in someembodiments, the frequency domain features utilized at 218 may comprisea normalized variant of the spectral spread and/or a normalized variantof the spectral centroid.

Referring still to FIG. 4 , in some embodiments, block 218 of method 200may comprise providing the plurality of frequency domain features to afluid inflow model (e.g., a logistic regression model) at 220 for eachof the gas phase, the aqueous phase, and the hydrocarbon liquid phase;and determining that at least one of the gas phase, the aqueous phase,or the hydrocarbon liquid phase is present based on the fluid inflowmodel. In some embodiments, the fluid inflow model utilized at block 220may be similar to the fluid outflow model utilized at block 212 andpreviously described above; however, the fluid inflow model at block 220may be tuned or constructed to detect the presence of absence of a fluidinflow (e.g., such as a gas phase fluid inflow, an aqueous phase fluidinflow, or a hydrocarbon liquid phase fluid inflow). Thus, the fluidinflow model can be developed using and/or may include machine learningsuch as a neural network, a Bayesian network, a decision tree, alogistical regression model, or a normalized logistical regression, orother supervised learning models. In some embodiments, the fluid inflowmodel at 220 may define a relationship between at least two of theplurality of the frequency domain features, including in someembodiments combinations, variations, and/or transformations of thefrequency domain features and one or more fluid flows. In someembodiments, block 220 may comprise utilizing a plurality of differentmodels to identify each type of fluid inflow (e.g., gas, aqueous,hydrocarbon liquid, etc.). For instance, block 218 may compriseutilizing a first fluid inflow model to identify a gas phase inflow, asecond model to identify an aqueous phase fluid inflow, and a thirdmodel to identify a hydrocarbon liquid phase fluid inflow. In someembodiments, one or more of the first, second, and third models maycomprise multivariable models. In some of these embodiments, the first,second, and third models may utilize one or more frequency domainfeatures (which may or may not be the same for each of the first,second, and third models) as inputs therein.

In some embodiments, block 218 (e.g., such as block 220) may compriseutilizing the plurality of frequency domain features at the identifiedone or more fluid inflow locations in the model(s) (e.g., the first,second, third model as described above) and then comparing the pluralityof frequency domain features to an output of the model(s); andidentifying at least one of the gas phase inflow, the aqueous phaseinflow, or the hydrocarbon liquid phase inflow based on thecomparison(s).

Referring still to FIG. 4 , method 200 may further comprise determiningamounts of gas phase inflow, aqueous phase inflow, and hydrocarbonliquid phase inflow at 222. In particular, determining the amounts ofthe types of fluid flow/inflow (e.g., gas, aqueous, hydrocarbon liquid,etc.) may comprise determining a total fluid inflow rate for thewellbore and then allocating the total fluid inflow rate across the oneor more fluid inflow locations. For instance, the total fluid inflowflow rate may be determined via measurement from one or more flow metersor sensors (e.g., similar to that described above for measuring thetotal injected volumetric flow rate for block 214) that me be separatefrom the DAS system (e.g., monitoring system 110 in FIG. 1 ), and thendetermining (e.g., via one or more of frequency domain features) therelative contributions to the total fluid inflow rate from each of theof the plurality of fluid inflow locations.

In some embodiments, block 222 may comprise classifying the flow rate(e.g., in volume per unit time—such as barrels per day) of eachidentified fluid inflow type into one of a plurality of predefined flowrate ranges. The predefined flow rate ranges can be determined for eachtype of flow corresponding to the flow model. For example, a first setof predefined flow rate ranges can be determined for gas inflow, asecond set of predefined flow rate ranges can be determined for aqueousinflow, and a third set of predefined flow rate ranges can be determinedfor hydrocarbon liquid inflow. These various predefined flow rate rangescan then be used with a labeled data set (e.g., frequency domainfeatures sets with known, or labeled, inflow rate that can be derivedfrom test data, known historical data, etc.) to determine models foreach of the inflow rate ranges and fluid types.

In some embodiments, the plurality of predefined ranges may comprise aplurality of pre-defined ranges corresponding with low, medium, and highinflow rates for each of the identified fluid inflows. However, in otherembodiments, the plurality of pre-defined ranges may correspond withother flow rates (i.e., other than low, medium, and high). In someembodiments, the predefined flow rate ranges may be selected to indicate(e.g., to personnel monitoring production from the well) whether certainproduction conditions or parameters are being met. In some embodiments,the predefined flow rate ranges may be selected so as to indicate (againto suitable personnel or other machine implemented monitoringapplications) that desired and/or problematic production conditions(e.g., with respect to production amounts of the identified fluids) arepresent. Thus, in some embodiments, the predefined flow rate ranges mayhave different magnitudes, scopes, boundaries, etc. In some embodiments,at least some of the predefined inflow rate ranges may have an equalscope or magnitude. In some embodiments, the predefined flow rate rangesmay not include a zero-flow condition such that the predefined flow rateranges may include and be bounded by values that are greater than zero.The size, scope, magnitude, and number of predefined flow rate rangesmay be selected and varied in some embodiments due to the specificparameters of the wellbore in question (e.g., wellbore 114 in FIG. 1 ),and/or the desired flow rate conditions that are being monitored for thewellbore in question.

The flow rate models can be developed using and/or may include machinelearning such as a neural network, a Bayesian network, a decision tree,a logistical regression model, or a normalized logistical regression, orother supervised learning models with known labeled data sets. In someembodiments, the flow rate models may each define a relationship betweenat least two of the plurality of the frequency domain features,including in some embodiments combinations, variations, and/ortransformations of the frequency domain features and a flow rate for aspecific fluid type. A plurality of models can then be developed foreach fluid type that corresponds to each flow rate range in thepredefined flow rate ranges for that fluid type. The flow rate modelsmay each utilize one or more (e.g., at least two) of the frequencydomain features as inputs, which may or may not be the same for each ofthe models within a fluid type or for the models across different fluidflow types.

In some embodiments, block 222 may comprise using one more (e.g., aplurality of) the frequency domain features described above to classifythe flow rate of the fluids inflows identified at block 218.Specifically, in some embodiments, one or more of the flow rate models,utilizing one or more frequency domain features as inputs, may be usedat block 222 to classify the flow rate of each identified fluidflow/inflow into the predefined ranges. For instance, as is similarlydescribed above for block 218 (including block 220), block 222 maycomprise utilizing a separate, different model (or a plurality ofseparate models) for classifying the flow rate of each identified fluidinflow. Thus, a first flow rate model (or a plurality of first flow ratemodels) may be used to classify the flow rate of an identified gasinflow, a second flow rate model (or a plurality of second flow ratemodels) may be utilized to classify the flow rate of an identifiedaqueous inflow, and a third flow rate model (or a plurality of thirdflow rate models) may be utilized to classify the flow rate of anidentified hydrocarbon inflow. Each of the first, second, and third flowrate models at block 222 may use one or more, such as at least two (or aplurality of) the above described frequency domain features (includingas previously described, combinations, transformations, and/or variantsthereof) as inputs. The frequency domain features used in each of thefirst, second, and third models at block 222 may be the same ordifferent. Additionally, as will be described in more detail below, theflow rate model(s) used to classify the fluid flow rate(s) at block 222may be derived via machine learning, such as, for instance a supervisedmachine learning process whereby known experiment data is utilized toconstruct and/or refine the model(s).

In addition, in some embodiments, each of the first, second, and thirdflow rate models described above may include a plurality of flow ratemodels—each to determine whether the flow rate of the particular fluidin question falls within a plurality of predetermined flow rate ranges.Thus, the first flow rate model may comprise a plurality of first flowrate models where each of the first flow rate models may determinewhether the flow rate of the gas inflow is within a corresponding one ofthe plurality of predefined flow rate ranges, based on a selectedplurality of frequency domain features (which may be the same ordifferent for the plurality of first models). Likewise, the same may betrue for the second and third flow rate models, such that the secondflow rate model and third flow rate model may comprise a plurality ofsecond flow rate models and a plurality of third flow rate models,respectively, for determining whether the flow rate of the aqueousinflow and hydrocarbon liquid inflow, respectively, fall within aplurality of predetermined flow rate ranges.

In some embodiments, the flow rate model(s) used to classify the flowrates of the identified fluid inflows at block 222 may define decisionboundaries using two or more frequency domain features. Each decisionboundary may determine whether a type of identified fluid inflow (e.g.,gas, aqueous, hydrocarbon liquid, etc.) has a flow rate that is within aparticular flow rate range. Thus, in embodiments where there are twoflow rate ranges for each identified fluid inflow, the flow ratemodel(s) may construct two decision boundaries for each identified fluidflow/inflow—one for determining whether a particular type of fluid has aflow rate in a first flow rate range, and a second for determining theparticular type of fluid has a flow rate in a second flow rate range,where the first flow rate range is different from the second flow raterange (e.g., higher, lower, etc.).

Each decision boundary may be based on two or more selected frequencydomain features. For instance, in some embodiments a flow rate modelutilized at block 222 may mathematically define a decision boundary as aline in two dimensional space where the axes of the two dimensionalspace are defined by two selected frequency domain features. In otherembodiments, a flow rate model utilized at block 222 may construct ordefine a decision boundary as a three-dimensional surface where the axesof the three dimensional space are defined by three selected frequencydomain features. Regardless of the number of frequency domain featuresutilized by the models at block 222, when points are plotted in thedimensional space defined by the selected frequency domain features(e.g., a 2, 3, 4, 5, .. . N dimensional space determined by the numberof selected frequency domain features), the position of plotted pointsin the dimensional space (e.g., plotted points of the selected frequencydomain features) with respect to the decision boundary may determinewhether a type of fluid does or does not have a flow rate within aparticular flow rate range. The frequency domain features selected toconstruct the decision boundaries associated with the predetermined flowrate ranges for a particular type of identified fluid may be the same ordifferent. In some embodiments, one or more of the axes of thedimensional space containing a particular decision boundary may comprisea combination, variation, and/or transformation of a frequency domainfeature as previously described above.

The classification at block 222 of the flow rates for the inflow of thefluids identified at block 600 may be carried out for flow rates over apredetermined period of time (e.g., a period of second, minutes, hours,days, weeks, months, etc.). The predetermined period of time maycomprise the entire producing life of the well (e.g., such as wellbore114 in FIG. 1 ) or some period that is less than the entire working lifeof the well. Specifically, the period of time associated with theacoustic signal at block 202, and thus the period of time associatedwith the selected frequency domain features from block 206 may definethe period of time over which the flow rates of the identified fluidtypes may be classified at block 222.

In some embodiments, for a given time period the classified flow rate ofa given fluid (e.g., gas, aqueous, hydrocarbon liquid) may fluctuatebetween multiple predetermined flow rate ranges. In these embodiments,the model(s) may present a dominant flow rate range as the flow rate forthe given fluid over the designated period of time. As used herein, thedominant flow rate range over a given period of time may represent theflow rate range that the given fluid most often was classified intoduring the given period of time. As one specific example, a given fluidmay be classified into a first flow rate range for a first portion of agiven period of time, and is classified into a second flow rate rangefor a second portion of the given period of time. If the first portionis greater than the second portion, the first flow rate range may bedetermined to be the dominant flow rate range over the entire givenperiod of time.

In addition, the classification at block 222 of the flow rates for theinflow of the fluids identified at block 218 may be carried out for flowrates over an entire depth of a wellbore (e.g., wellbore 114 in FIG. 2 )or at one or more discrete depths or depth ranges within the wellbore.Specifically, the classification at block 32 may classify different flowrates at different depths (or depth ranges) within a wellbore byanalyzing the frequency domain features (e.g., within the one or modelsas described above) associated with the different depths (or depthranges). Accordingly, via the classification at block 32, one maydetermine an overall flow rate range for a particular fluid type (e.g.,gas, aqueous, hydrocarbon liquid, etc.) over an entire depth of a givenwellbore, and/or may classify flow rates for a particular fluid type ata plurality of different depths (or depth ranges) within the givenwellbore.

In some embodiments, the model(s) at blocks 210, 212, 218, 220, 222 ofmethod 200 can be developed using machine learning. In order to developand validate the model, data having known fluid flows (including fluidtype, flow rate, and inflow location) and acoustic signals can be usedas the basis for training and/or developing the model parameters. Thisdata set can be referred to as a labeled data set (e.g., a data set forwhich the flow regime, outflow or inflow location, and/or flow rates isalready known) that can be used for training the models in someinstances. In some embodiments, the known data can be data from awellbore having flow characteristics measured by various methods. Insome embodiments, the data can be obtained using a test setup whereknown quantities of various fluids (e.g., gas, hydrocarbon liquids,aqueous liquids, etc.) can be introduced or emitted at one or morecontrolled points to generate controlled fluid flows, outflows, and/orinflows. At least a portion of the data can be used to develop themodel, and optionally, a portion of the data can be used to test themodel once it is developed.

Referring now to FIG. 5 , a flow chart of a method 300 of characterizinga fluid outflow from a wellbore and into a surrounding subterraneanformation according to some embodiments of this disclosure is shown.Generally speaking, method 300 may be utilized to characterize fluidflowing out of a wellbore and into the surrounding formation using oneor more frequency domain features obtained from an acoustic signaloriginating within the wellbore and one or more temperature featuresobtained from a temperature signal originating within the wellbore. Asdescribed in more detail below, the acoustic and temperature signals mayprovide a well operator with valuable information regarding the fluidflows out of the wellbore and into the formation, which may be usefulduring certain wellbore operations (e.g., such as an injectionoperation).

Initially, method 300 includes obtaining one or more frequency domainfeatures from an acoustic signal originating within a wellbore extendinginto a subterranean formation at block 302, identifying one or morefluid outflow locations within the wellbore using the one or morefrequency domain features at block 304, and determining an allocation ofa total injected fluid flow across the one or more fluid outflowlocations using the one or more frequency domain features at block 306.For instance, in some embodiments, blocks 302, 304, 306 may comprise thesame steps and features discussed above for blocks 202, 206, 210, 212,and 214 of method 200 (and may possibly include the additional steps ofblocks 204, 208 as previously described above). As a result, a detaileddescription of these features is not repeated herein for blocks 302,304, 306 of method 300 so as to simplify the description and promoteconciseness and brevity. Therefore, the one or more fluid outflowlocations may be identified at block 304 and allocation of the totalinjected volume among the one or more fluid outflow locations may bedetermined via one or more fluid models (e.g., fluid outflow models)that utilize one or more of the frequency domain features of theacoustic signal as inputs in the manner described.

In addition, in some embodiments, method 300 includes shutting in thewellbore at 308. Shutting in the wellbore (e.g., wellbore 114) may occurimmediately after block 306 or may occur after receiving the acousticsignal from block 302. Thus, the precise timing of shutting the wellbore114 may be greatly varied in different embodiments. In some embodiments,shutting in the wellbore at block 308 may comprise stopping flow out ofor into the wellbore 114, and may involve closing one or more valves orother fluid control devices within or coupled to the wellbore (e.g.,such as coupled to the tubular 120 in the wellbore 114 of FIG. 1 ).Following shutting in the wellbore at block 308, fluid flow into or outof the wellbore at the surface are prevented; however, it may bepossible that fluid may continue to migrate between the formation andwellbore (e.g., such as between formation 102 and the wellbore 114,particularly tubular 120 for the wellbore 114 of FIG. 1 ) after thewellbore is shut in at block 308.

Next, method 300 includes obtaining one or more temperature featuresfrom a temperature signal originating within the wellbore at block 310.The temperature features may be determined from using a distributedtemperature sensing signal within the wellbore. For example, thetemperature features can be determined using the monitoring system 110shown in FIG. 1 and described above (or the monitoring systems 110A,110B in FIG. 3 ) to obtain temperature measurements along the monitoredlength (e.g., a monitored length along the optical fiber 162, such asalong a length of the wellbore 114). In some embodiments, a monitoringsystem 110 can be used to receive distributed temperature measurementsignals from a sensor disposed along the length (e.g., of a wellbore114), such as optical fiber 162 (see e.g., FIG. 1 and the associateddescription above). The resulting signals from the monitoring system 110can be used to determine one or more temperature features as describedherein. In some embodiments, a baseline or background temperatureprofile can be used to determine the temperature features, and thebaseline temperature profile can be obtained prior to obtaining thetemperature measurements. In some embodiments, the temperature signal(from which the one or more temperature features are obtained) at block310 may be obtained after the wellbore is shut in at block 308. In someembodiments, a plurality of temperature features can be determined fromthe temperature measurements, and the plurality of temperature featurescan comprise one or more (e.g., a plurality of) any of the temperaturefeatures previously described above including combinations, variants(e.g., a normalized variant), and/or transformations thereof.

In some embodiments, the temperature signal may be obtained aftershutting in the wellbore at block 308 as generally indicated in FIG. 5 .Thus, the temperature signal may be collected at a time when fluid isnot flowing into or out of the wellbore from the surface (i.e., no fluidis being injected into the wellbore from the surface or produced fromthe wellbore at the surface).

Once the temperature features are obtained, method 300 includesidentifying one or more fluid uptake locations within the subterraneanformation using the temperature features within the wellbore at 312. Theuptake locations can generally include areas in the near wellboreregion, including the area surrounding the wellbore such that atemperature differential within the near wellbore region can be detectedthrough conduction or convective flow of fluids in to the wellbore.Referring briefly again to FIG. 1 , and as generally described above, afluid uptake location within the subterranean formation 102 may comprisea zone or area (e.g., zones 104 a, 104 b) within formation 102 that mayreceive fluid that is output from the wellbore 114 (e.g., from tubular120). For instance, as previously described above for the wellbore 114of FIG. 1 , fluid that is injected into wellbore 114 may be emitted fromtubular 120 into annulus 119, which may be separated into a plurality ofintervals via the zonal isolation devices 117 (e.g., packers). Thus, thefluid uptake locations within formation 102 may be the ultimate point orlocation where the fluid entering the annulus 119 is flowing into theformation 102 itself. In some embodiments, the fluid uptake locationsmay comprise the (or a portion of) the production zones 104 a, 104 b,including those within the near wellbore region. In some embodiments,the fluid uptake locations may comprise perforations or fractures in thewall of the wellbore 114 (e.g., such as perforations or fractures formedby a previous perforating or hydraulic fracturing operation within thewellbore 114).

Without being limited to this or any other theory, injected fluid thathas flowed into the formation may begin to have a pronounced effect onthe temperature of the formation and wellbore, particularly at thelocations or depths within the wellbore where fluid was flowed into theformation (e.g., at the one or more fluid uptake locations within thenear wellbore region). For instance, referring briefly again to thewellbore 114 of FIG. 1 , the ambient temperature of the wellbore 114 andformation 102 may be generally higher than at the surface—especially forlocations deep within the wellbore (e.g., such as at production zones104 a, 104 b). As a result, an injected fluid into the wellbore 114 maybe at a generally lower temperature than both the wellbore 114 andformation 102. As the fluid flows through the tubular 120 and into theformation 102 at the one or more uptake locations, the temperature ofthe wellbore 114 and formation immediately surrounding the wellbore 114in the near wellbore region (e.g., at least at and around the one ormore uptake locations) may cool. These changes in temperature can thenbe used (e.g., as described herein) to determine where the one or morefluid uptake locations are located and possibly how much fluid isentering the formation (e.g., as a total volume, flow rate, etc.) at theone or more uptake locations.

As previously described, a distributed acoustic signal (e.g., such maybe obtained from a DAS system) may provide an indication of fluidoutflow via the vibrations and acoustic sounds resulting from the flowof fluid out of a tubular. However, in some instances, thesemeasurements do not provide much information with respect to how thefluid is then flowing into a formation (e.g., formation 102). Becausethe injection of fluid into the formation 102 may have an effect on thetemperature profile within the wellbore 114 as previously described, theadditional distributed temperature signal provided by a DTS system(e.g., monitoring system 110) may provide additional insight as to theultimate uptake of the fluid into the formation 102 following the exitof the fluid from the tubular member of the wellbore 114 (e.g., fromtubular member 120).

Thus, referring again to FIG. 5 , in some embodiments, determining theone or more fluid uptake locations may comprise providing the one ormore temperature features as inputs to a fluid outflow model at block314. In general, the temperature features are representative of featuresat a particular location (e.g., a depth resolution portion of theoptical fiber along a length (e.g., a length of the wellbore)) along thewellbore 114. The fluid outflow model at block 314 can comprise one ormore models configured to accept the temperature features as input(s)and provide an indication of whether or not a fluid is flowing into theformation at the particular location along the length of the opticalfiber 162 and/or wellbore 114. The output of the fluid outflow model atblock 314 can be in the form of a binary yes/no result, and/or alikelihood of an event (e.g., a percentage likelihood, etc.). Otheroutputs providing an indication of a fluid uptake location are alsopossible. In some embodiments, the fluid model can comprise amultivariate model, a machine learning model using supervised orunsupervised learning algorithms, or the like. Thus, the fluid outflowmodel at block 314 may be similar to the fluid outflow model utilized atblock 304 (and/or block 212 of method 200), except that the fluidoutflow model of block 314 may utilize the one or more temperaturefeatures as inputs rather than one or more frequency domain features.

More specifically, the fluid outflow model at block 314 may, in someembodiments, comprise a multivariate model or a plurality ofmultivariate models. A multivariate model allows for the use of aplurality of variables in a model to determine or predict an outcome. Amultivariate model can be developed using known data for a fluid uptakeinto the formation along with temperature features therefor to develop arelationship between the temperature features and the occurrence offluid uptake at the locations within the available data. One or moremultivariate models can be developed using data, where each multivariatemodel uses a plurality of temperature features as inputs to determinethe likelihood of fluid uptake occurring at the particular locationalong the length of the wellbore and/or optical fiber (e.g., opticalfiber 162).

The multivariate model(s) of block 314 can use multivariate equations,and the multivariate model equations can use the temperature features orcombinations or transformations thereof to determine when fluid uptakeis (or is not) occurring. The multivariate model(s) can definethresholds, decision points, and/or decision boundaries having any typeof shapes such as a point, line, surface, or envelope between thepresence and absence of fluid uptake. In some embodiments, themultivariate model can be in the form of a polynomial, though otherrepresentations are also possible. The model can include coefficientsthat can be calibrated based on known data. While there can bevariability or uncertainty in the resulting values used in the model,the uncertainty can be taken into account in the output of the model.Once calibrated or tuned, the model can then be used with thecorresponding temperature features to provide an output that isindicative of the occurrence (or lack of occurrence) of a fluid uptakeinto the formation (e.g., formation 102) at one or more locations.

The multivariate model is not limited to two dimensions (e.g., twotemperature features or two variables representing transformed valuesfrom two or more temperature features), and rather can have any numberof variables or dimensions in defining the threshold between thepresence or absence of fluid uptake within the formation. When used, thedetected values can be used in the multivariate model, and thecalculated value can be compared to the model values. In someembodiments, the output of the multivariate model(s) can be based on avalue from the model(s) relative to a normal distribution for themodel(s). Thus, the model can represent a distribution or envelope andthe resulting temperature features can be used to define where theoutput of the model lies along the distribution at the location in thewellbore. Thus, each multivariate model can, in some embodiments,represent a specific determination between the presence of absence offluid uptake at a specific location along a length of the wellbore.

In some embodiments, the fluid outflow model of block 314 can alsocomprise other types of models. In some embodiments, a machine learningapproach comprises a logistic regression model. In some suchembodiments, one or more temperature features can be used to determineif fluid uptake into the formation 102 is occurring at one or morelocations of interest. The machine learning approach can rely on atraining data set that can be obtained from a test set-up (e.g., a flowloop) or obtained based on actual temperature data from known fluiduptake events. The one or more temperature features in the training dataset can then be used to train the model using machine learning,including any supervised or unsupervised learning approach. For example,the fluid model can be a neural network, a Bayesian network, a decisiontree, a logistical regression model, a normalized logistical regressionmodel, or the like. In some embodiments, the fluid outflow model ofblock 314 can comprise a model developed using unsupervised learningtechniques such a k-means clustering and the like.

Method 300 also includes determining an allocation of the total injectedfluid volume across the one or more fluid uptake locations at block 316.As previously described, the one or more temperature features obtainedfrom the temperature signal at block 310 may be utilized to identify oneor more fluid uptake locations within a subterranean formation at block312. Additionally, the one or more temperature features can also beutilized to determine how much of a total injected fluid volume wasflowed into the formation at each of the identified fluid uptakelocations at block 316. For instance, referring again to the wellbore114 in FIG. 1 , the temperature of the injected fluid may be less than(or at least different) than the formation 102 and wellbore114—especially at depths typically associated with hydrocarbonproduction. Thus, upon injecting the fluid through the wellbore 114(particularly tubular 120) and into formation 102, the temperaturewithin the wellbore 114 and the portions of formation 102 surroundingwellbore 114 may cool. Once the fluid injection is stopped, the ambienttemperature within the formation 102 will begin to again increase thetemperature within the wellbore 114 back to ambient conditions. However,if a portion of the formation 102 receives the injected fluid (e.g., atone or more of the fluid uptake locations), the temperature rise of thatportion of the formation 102 as well as the section or depth intervalwithin the wellbore 114 corresponding to this portion of the formation102 may see a more gradual or slower increase in temperature over timeas compared to other portions of the formation 102 (and thecorresponding sections or depth intervals within the wellbore 114through these other portions) that did not receive any or a lesssignificant portion of the injected fluid. Accordingly, referring backto FIG. 5 , by capturing the temperature signal after shutting in thewellbore at block 308, the temperature features may provide anindication (after appropriate analysis within one or more fluid flowmodels) of not only where the injected fluid entered the formation alongthe length of the wellbore (e.g., so as to identify the one or morefluid uptake locations), but also of how much (in a relative sense) ofthe injected fluid was flowed into the identified fluid uptakelocations.

Accordingly, in some embodiments, the one or more temperature featuresmay be input into a fluid flow model at block 316 (e.g., which maycomprise another multivariate model or plurality of multivariate modelsas described above for blocks 312, 314) so as to provide an indicationof the amount of fluid that may have been received at each uptakelocation at block 316. In some embodiments, the amount of fluiddetermined by the fluid flow model at block 316 may comprise anallocation (e.g., percentage, fraction, etc.) of a known total injectedvolume into the wellbore 114 across each of the identified one or morefluid uptake locations. The total injected fluid volume may bedetermined in any of the manners previously described above for block214 of method 200 in FIG. 4 . Because the well may be shut in at block308 following the injection of fluid, the total injected fluid volumemay be a static, total volume of fluid that was previously injectedbefore the well was shut in. In some embodiments, the one or moretemperature features used at block 316 may provide an indication of atemperature change over time along the length of the wellbore 114, andthe temperature change may be utilized (e.g., via the fluid model) todetermine an allocation of the total injection fluid volume amongst thefluid uptake locations within the formation.

In some embodiments, the fluid models in blocks 314 and 316 may compriseseparate models utilizing separate inputs (e.g., different selectionsand/or combinations of the one or more temperature features) andproviding separate outputs. In some embodiments, the fluid models inblocks 314 and 316 may comprise a combined model that provides both anidentification of the one or more fluid uptake locations and anallocation of the total injected fluid volume amongst the identifiedfluid uptake locations.

Referring now to FIG. 6 , a flow chart of a method 350 of characterizinga fluid outflow from a wellbore according to some embodiments is shown.Generally speaking, method 350 comprises characterizing a fluid outflowfrom the wellbore using both an acoustic signal and a temperature signaloriginating within the wellbore. Without being limited to this or anyother theory, by characterizing the fluid outflows from a wellbore usingboth an acoustic signal and a temperature signal, more accurateconclusions may be drawn so as to further enhance a well operator'sability to manage downhole operations within the wellbore.

Specifically, method 350 may comprise determining one or moretemperature features from a distributed temperature signal originatingwithin the wellbore at block 352, and determining one or more frequencydomain features from an acoustic signal originating within the wellboreat block 354. The temperature features can be determined at 352 usingany of the processes and systems as described herein (see e.g., block310 of method 300 in FIG. 5 ). In some embodiments, a DTS system (e.g.,monitoring system 110 in FIG. 1 ) can be used to obtain distributedtemperature sensing signal within the wellbore. The DTS system canprovide distributed temperature measurements within the wellbore overtime. A baseline temperature can be stored for the wellbore as describedherein and used along with the temperature measurements to determine thetemperature features. The temperature features can include any of thosedescribed herein including a depth derivative of temperature withrespect to depth, a temperature excursion measurement, a baselinetemperature excursion, a peak-to-peak value, a statistical measure of avariation with respect to time and/or distance, or a combinationthereof.

Similarly, the frequency domain features can be determined using any ofthe processes and systems as described herein (see e.g., block 206 ofmethod 200 in FIG. 4 ). In some embodiments, a DAS system (e.g.,monitoring system 110 in FIG. 1 ) can be used to obtain a distributedacoustic signal within the wellbore. The acoustic signals obtained fromthe DAS system can then be processed to determine one or more frequencydomain features as described herein. In some embodiments, the frequencydomain features can comprise at least one of: a spectral centroid, aspectral spread, a spectral roll-off, a spectral skewness, an RMS bandenergy, a total RMS energy, a spectral flatness, a spectral slope, aspectral kurtosis, a spectral flux, a spectral autocorrelation function,or any combination thereof, including combinations and modificationsthereof.

Next, method 350 includes using the one or more temperature features andthe one or more frequency domain features to identify one or more fluidoutflow locations along the wellbore at block 356. For instance thetemperature features and the frequency domain features can be inputtedinto one or more fluid outflow models that may then identify thepresence or absence of fluid outflow at one or more locations along thelength of the wellbore (e.g., wellbore 114). The fluid model(s) may besimilar to any of the other fluid models discussed herein (see e.g.,blocks 212, 220 in FIG. 4 , block 314 in FIG. 5 , etc.), and thus, maycomprise one or more multivariate models that utilize the one or moretemperature features and/or the one or more frequency domain features toidentify the one or more fluid outflow locations from the wellbore(e.g., locations where the fluid is flowing out of tubular 120 intoannulus 119 as previously described for FIG. 1 ).

In some embodiments, the one or more temperature features and the one ormore frequency domain features may be inputted together into a singlemodel or set of models making up the fluid outflow model. In someembodiments, the one or more temperature features may be inputted into afirst model (or group of first models), the one or more frequency domainfeatures may be inputted in a second model (or group of second models),and the outputs of the first model(s) and the second model(s) may becombined to form a final output identifying the one or more fluidoutflow locations within wellbore. Any suitable functions can be used tocombine the outputs of the first model(s) and the second model(s). Thiscan include formulas, products, averages, and the like, each of whichcan comprise one or more constants or weightings to provide the finaloutput. The ability to determine the fluid outflow locations as afunction of the output of both models can allow for either model (orgroup of models) to override the output of the other model (or group ofmodels). For example, if the one model indicates that a location alongthe wellbore comprises a fluid outflow location, but the other modelindicates no fluid outflow, the resulting combined output may beconsidered to indicate that there is no fluid outflow at that location.Thus, the use of the hybrid model approach can provide two separate waysto verify and determine the fluid outflow locations from the wellbore.

In some embodiments, the preprocessing of the temperature or acousticsignals may occur before determining the one or more temperaturefeatures and/or the one or more frequency domain features. For instance,in some embodiments, similar preprocessing steps may be carried out aspreviously described above for block 204 in method 200 in FIG. 4 . Inaddition, in some embodiments, the one or more temperature features andthe one or more frequency domain features may be normalized prior toidentifying the one or more fluid outflow locations at block 356. Forinstance, in some embodiments, similar normalization step(s) may becarried out as previously described above for block 208 of method 200 inFIG. 4 .

In some embodiments, method 350 may also include measuring a fluid flowrate from the wellbore at block 358 and determining an allocation of thefluid flow rate across the one or more fluid out flow locations at block360. The fluid flow rate may be measured via any of the methodspreviously described above (e.g., see e.g., block 214 in method 200 ofFIG. 4 ). For instance, the fluid inflow rates can be refined by using ameasure of the fluid flow rate from the wellbore as measured at loggingtool above the producing zones, a wellhead, surface flow line, or thelike.

In addition, allocating the fluid flow rate across the one or more fluidflow locations may comprise inputting the one or more temperaturefeatures and/or the one or more frequency domain features to a fluidoutflow model, which may comprise the same or a different fluid model asdescribed above for block 356. In some embodiments, block 360 maycomprising determining the allocation or amounts in any one or more ofthe manners described above for blocks 214, 222 of method 200 in FIG. 4. If a fluid flow model is utilized to determine the allocation at block360, the fluid flow model (or group of models) may be derived and usedin a similar fashion to the other fluid models described herein (seee.g., block 356).

Referring now to FIG. 7 , a method 400 of characterizing the fluidsflows of a fluid injection operation between a pair of wellboresextending within a subterranean formation (e.g., formation 102) isshown. Generally speaking, method 400 may be performed so as tocharacterize one or more of the fluid flows into, through, and/or out ofa subterranean formation during a fluid injection operation. In someaspects, the fluid flow characterization can be used to help to improvethe injection and production of the wellbore and/or improve a reservoirmodel used to help to control injection and production within thereservoir. For instance, referring briefly again tot FIG. 3 , a fluidmay be injected into formation 102 via a first wellbore 114A, which thenresults in fluid (e.g., injected fluid, formation fluid, both, etc.)being produced into the second wellbore 114B. As will be described inmore detail below, the embodiments of method 400 may be performed so asto identify and/or characterize the various fluid outflows from thefirst wellbore 114A, the fluid flow from the wellbore 114 into theformation 102, and the fluid inflows into the second wellbore 114B, etc.

Initially, method 400 includes obtaining a first acoustic signal from afirst sensor within a first wellbore, wherein the first acoustic signalcomprises acoustic samples across a portion of a depth of the firstwellbore at block 402. In addition, method 400 includes determining oneor more frequency domain features from the first acoustic signal atblock 404, and identifying one or more fluid outflow locations withinthe first wellbore using the one or more frequency domain features fromthe first acoustic signal at block 406.

The first acoustic signal obtained at block 402 may be obtained via asuitable monitoring system, such, for instance, monitoring system 110 inFIG. 1 and/or one of the monitoring systems 110A, 110B in FIG. 3 aspreviously described above. For instance, the monitoring system (e.g.,monitoring systems 110, 110A, 110B, etc.) may comprise an optical fiber(e.g., optical fiber 162, 162A, 162B, etc.) disposed within the wellboreand configured to measure or detect the first acoustic signal. Obtainingthe first acoustic signal at block 402 may thus be similar to the stepsand features of block 202 in method 200 previously described above.

Determining the one or more frequency domain features at block 404 andidentifying the one or more fluid outflow locations within the firstwellbore using the one or more frequency domain features at block 406may comprise similar steps and features as described above for blocks206 and 210 of method 200 in FIG. 4 . Thus, these features are notrepeated herein in the interests of brevity. However, it should beappreciated, as a result, that the one or more frequency domain featuresof the first acoustic signal may be inputted into a fluid outflow model(which may comprise one or a plurality of multivariate models aspreviously described) that then may provide an indication of thepresence or absence of the one or more fluid outflow locations withinthe first wellbore at a particular depth(s) or range(s) of depths.

In addition, in some embodiments method 400 may also includepreprocessing the first acoustic signal and/or normalizing the one ormore frequency domain features of the first acoustic signal in a similarmanner to that described above for blocks 205 and 208 of method 200 inFIG. 4 . Further, in some embodiments, method 400 may also compriseshutting in the wellbore, obtaining a temperature signal (e.g., via amonitoring system 110, 110A, 110B, etc. as previously described), aftershutting in the wellbore, and identifying one or more fluid uptakelocations within the formation (e.g., formation 102) surrounding thefirst wellbore in a similar manner to that described above for blocks308-316 of method 300 in FIG. 5 . Still further, in some embodiments,method 400 may also comprise determining an allocation of a totalinjected fluid flow across the one more fluid outflow locations withinthe first wellbore in a similar manner that that described above forblock 214 of method 200 in FIG. 4 , and/or determining an allocation ofa total injected fluid volume across the one or more fluid uptakelocations within the formation (e.g., formation 102) in a similar mannerto that described above for block 316 of method 300 in FIG. 5 .

Referring still to FIG. 7 , method 400 next includes obtaining a secondacoustic signal from a second sensor within a second wellbore, whereinthe second acoustic signal comprises acoustic samples across a portionof a depth of the second wellbore at block 408. In addition, method 400includes determining one or more frequency domain features from thesecond acoustic signal at block 410. Further, method 400 includesidentifying one or more fluid inflow locations within the secondwellbore using the one or more frequency domain features from the secondacoustic signal at block 412.

The second acoustic signal obtained at block 408 may be obtained via asuitable monitoring system, such, for instance, monitoring system 110 inFIG. 1 and/or one of the monitoring systems 110A, 110B in FIG. 3 aspreviously described above. For instance, the monitoring system (e.g.,monitoring systems 110, 110A, 110B, etc.) may comprise an optical fiber(e.g., optical fiber 162, 162A, 162B, etc.) disposed within the secondwellbore and configured to measure or detect the first acoustic signal.Obtaining the first acoustic signal at block 402 may thus be similar tothe steps and features of block 202 in method 200 previously describedabove.

Determining the one or more frequency domain features at block 408 andidentifying the one or more fluid inflow locations within the firstwellbore using the one or more frequency domain features at block 410may comprise similar steps and features as described above for blocks206 and 218 of method 200 in FIG. 4 . Thus, these features are notrepeated herein in the interests of brevity. However, it should beappreciated, as a result, that the one or more frequency domain featuresof the second acoustic signal may be inputted into a fluid inflow model(which may comprise one or a plurality of multivariate models aspreviously described) that then may provide an indication of thepresence or absence of the one or more fluid inflow locations within thefirst wellbore at a given depth or ranges of depths. In addition, aspreviously described above for block 218 of method 200, identifying theone or more fluid inflow locations at block 412 may comprise identifyingat least one of a gas phase inflow, an aqueous phase inflow, or ahydrocarbon liquid phrase inflow at the one or more fluid inflowlocations in the manner previously described above.

In addition, in some embodiments method 400 may also includepreprocessing the second acoustic signal and/or normalizing the one ormore frequency domain features of the second acoustic signal in asimilar manner to that described above for blocks 205 and 208 of method200 in FIG. 4 . In addition, in some embodiments, method 400 maycomprise determining amounts of fluid inflow (e.g., flow rates, totalfluid amounts, etc.) at the one or more fluid inflow locations. In someembodiments, this may involve determining amounts of the one or more ofthe gas phase inflow, aqueous phase inflow, and the hydrocarbon liquidphase inflow in the manners previously described above for block 222 ofmethod 200 in FIG. 4 .

Once the outflow and inflow locations are determined as describedherein, the information can be used in a number of ways. In someaspects, the outflow and inflow locations can be used to improve oradjust the parameters of one or more reservoir models. The reservoirmodels generally provide a model of the reservoir and the reservoirproperties. The model allows for production scenarios to be run andtested to improve the production from the wellbore, including modelingof various processes such as secondary and tertiary recovery processes.In general, the models contain a number of assumptions about thereservoir properties that are often based on test data within the wellsin the reservoir. As the parameters can change over time, the ability touse the information on the outflow locations and amounts and the inflowlocations and amounts can be used to adjust the parameters within thereservoir models to more accurately represent the reservoir propertiesover time. Thus, the determined properties from the method 400 can beused to update the model over the life of the production from thereservoir to help to optimize the drawdown of the hydrocarbons in thereservoir.

Referring now to FIG. 8 , a method 450 of identifying an event within asubterranean formation is shown. Generally speaking, during wellboreoperations, such as, for instance, during an injection operation,various events (e.g., such as so-called micros-seismic events) mayoccur. These events may include, for instance, opening or forming afracture within the formation. During various wellbore operations (e.g.,such as a fluid injection operation, production operations, hydraulicfracturing operations, etc.), fracture formation may change how andwhere fluid flows from, into, and through the formation. Some fractureformations or enlargements may reduce an effectiveness of the fluidinjection operation (or other type of operation), so that a welloperator may wish to have knowledge of when such events occur so thatsuitable remedial actions may be taken to prevent waste of time and/orresources. Thus, embodiments of method 450 may be performed so as tomonitor (e.g., via a monitoring system 110 as previously described) forthe occurrence of various events within the formation during the fluidinjection operation (which may include any operation whereby fluid isinjected within a subterranean wellbore—including secondary recoveryoperations, hydraulic fracturing, etc.), so that suitable correctiveaction may be taken.

Initially, method 450 may comprise injecting a fluid into a wellboreextending into a subterranean formation at block 452 and receiving anacoustic signal from a sensor within the wellbore, wherein the acousticsignal comprises acoustic samples across a portion of the a depth of thewellbore at block 454. The acoustic signal received at block 454 may bereceived via a suitable monitoring system, such, for instance,monitoring system 110 in FIG. 1 and/or one of the monitoring systems110A, 110B in FIG. 3 as previously described above. For instance, themonitoring system (e.g., monitoring systems 110, 110A, 110B, etc.) maycomprise an optical fiber (e.g., optical fiber 162, 162A, 162B, etc.)disposed within the wellbore and configured to measure or detect theacoustic signal. Thus, obtaining the acoustic signal at block 454 maythus be similar to the steps and features of block 202 in method 200previously described above.

Next, method 450 includes determining one or more frequency domainfeatures from the acoustic signal at block 456 and determining anallocation of an injected volume of the fluid among a plurality ofoutflow locations using the one or more frequency domain features atblock 458. The frequency domain features obtained from the acousticsignal may be any one or more of the frequency domain features describedherein, including combinations, variants (e.g., a normalized variant),and/or transformations thereof.

In addition, determining the allocation of the injected fluid flowacross the plurality of outflow locations may be conducted in a similarmanner to that described above for block 214 of method 200 in FIG. 4 .Thus, a detailed description of these steps and features is not repeatedherein in the interests of brevity. In addition, in some embodiments,method 450 may include identifying one or more fluid outflow locationsusing the one or more frequency domain features in the manner describedabove for block 210 of method 200 in FIG. 4 . In some embodiments, theone or more fluid outflow locations may be known (e.g., based onwellbore construction, PLS data, etc.), prior to injecting the fluid atblock 452.

Next, method 450 includes receiving, at a first time, an indication of achange in the allocation at block 460. In some embodiments, receivingthe indication of the change at block 460 comprise continuouslydetermining the allocation as described above for block 458 (which mayinvolve repeated performance of blocks 456-458 in the manner previouslydescribed), and receiving an allocation that is sufficiently differentfrom a previous allocation or a plurality of previous allocations (e.g.,such as an average of the previous allocations). In some embodiments,the indication of a change in the allocation may comprise a change inthe allocation that is greater than a threshold (e.g., a total numericchange, a percentage change, etc.). In some embodiments, block 460 mayalternatively (or additionally) comprise receiving an indication of achange of a total fluid flow rate into the wellbore. Again, the changeof the total fluid flow rate may comprise a change that is above apredetermined threshold or limit. In various embodiments, receiving anindication of a change in the allocation (or a change in the totalinjected fluid flow rate) may be determined based on the acoustic signalobtained from within the wellbore at block 452. In some embodiments, atotal fluid flow rate may be adjusted (e.g., increased or decreased) soas to see if a chance in the allocation of the total fluid flow rateacross the one or more fluid outflow locations results.

Without being limited to this or any other theory, the indication of achange in ether the allocation of fluid flow across the one or morefluid outflow locations and/or a total fluid flow rate into the wellboremay indicate that an event has occurred within the subterraneanformation (e.g., subterranean formation 102). For instance, if afracture is formed or enlarged within the formation, injected fluid maytake a different flow path into and/or through the formation that maythen change the allocation of the total fluid flow rate out of thewellbore across the one or more outflow locations, and/or may alter(e.g., increase) a total injected fluid flow rate into the wellboreoverall. Thus, the indication of the change received at block 460 mayindicate that an event (e.g., such as a micro-seismic event) hasoccurred within the formation 102.

Next, after an indication of a change in the allocation is received atblock 462, method 450 includes storing a portion of the acoustic signalas a result of receiving the indication of the change, wherein theportion includes the first time, at block 462. For instance, if anindication of a change is not received at block 460, the acoustic signal(and the data associated therewith) may be deleted or overwritten (e.g.,such as during subsequent performance of blocks 454-460). However, as aresult of receiving an indication of a change, the portion of theacoustic signal is then saved on a suitable memory or memories (e.g.,memory 170 in FIG. 1 ) such that further analysis may be performedtherewith as described herein. In some embodiments, the portion of theacoustic data that is stored may comprise only those portion(s) of theacoustic data that are associated with depths within the wellbore wherethe allocation has changed.

Finally, after the portion of the acoustic signal is stored at block462, method 450 further includes identifying an event within thesubterranean formation using the portion of the acoustic signal 464. Aspreviously described, the change in the allocation or total injectedflow rate may signal that an event has taken place at or near the firsttime. As a result, the stored data maybe further analyzed to identifythe event. In some embodiments, the stored portion of the acousticsignal may be inputted to a fluid flow model that is to identify thepresence or absence of an event within the subterranean formation. Insome embodiments, one or more frequency domain features may be obtainedfrom the portion of the acoustic signal and submitted to a fluid model(which may comprise one or more multivariate models as previouslydescribed for many of the other fluid models described herein). Themodel may then output an indication of the presence or absence of theevent. In some embodiments, method 450 may comprise denoising theportion of the acoustic signal before identifying the event at block464.

In addition, in some embodiments, identifying the event comprisestriangulating the location of the event based on the portion of theacoustic signal. Specifically, because the acoustic signal may comprisea distributed acoustic signal across a portion of a depth of thewellbore (e.g., via an optical fiber 162) as previously described, theevent may be identified (e.g., via the fluid model as described above)by the portion of the acoustic data at a plurality of depths within thewellbore. As a result, the portion of the acoustic signal (and/orfrequency domain features thereof), at the plurality of depths may becompared (e.g., in the same or a different fluid flow model) so as todetermine, via triangulation, a likely location within the formation ofthe identified event.

Further, in some embodiments, method 450 may also comprise shutting inthe wellbore, obtaining a temperature signal (e.g., via a monitoringsystem 110, 110A, 110B, etc. as previously described) and identifyingone or more fluid uptake locations within the formation (e.g., formation102) surrounding the first wellbore in a similar manner to thatdescribed above for blocks 308-316 of method 300 in FIG. 5 . In some ofthese embodiments, determining the allocation at block 458 may comprisedetermining an allocation may comprise determining an allocation of atotal injected fluid volume across the one or more fluid uptakelocations either in lieu of or in addition to determining the allocationof the fluid flow rate across the one or more fluid outflow locations.In addition, in some of these embodiments, receiving the indication ofthe change at block 460 may comprise receiving an indication of a changefor the allocation across the one or more fluid outflow locations, theone or more fluid uptake locations, or both.

Still further, in some embodiments, method 450 may comprise receiving anacoustic and/or a temperature signal from a second wellbore, determiningone or more frequency domain features and/or temperature features,respectively, from the acoustic signal and/or the temperature signal,respectively, from the second wellbore, and confirming the event (e.g.,such as a fracture formation, enlargement, etc.) using the one or morefrequency domain features and/or temperature features from the acousticsignal and/or temperature signal from the second wellbore. These stepsmaybe similar to those previously described above for blocks 408-412 ofmethod 400 in FIG. 7 and elsewhere herein, and thus are not repeated inthe interests of brevity.

Once the event is identified and/or the location determined as describedherein, the information can be used in a number of ways. In someaspects, the type of event, the identification of the event, and/or alocation of the event can be used to improve or adjust the parameters ofone or more reservoir models, as described herein. In general, themodels contain a number of assumptions about the reservoir propertiesincluding the location of features within the. As the events are trackedover time, the reservoir model can be updated to take the identifiedevents and event locations into account. For example, a detectedmicroseismic event can be used to indicate a fluid flow location, achange in the structure of the reservoir, or the like. Thus, theinformation on the events from the method 450 can be used to update themodel over the life of the production from the reservoir to help tooptimize the drawdown of the hydrocarbons in the reservoir.

FIG. 9 illustrates a flow diagram of a method 500 of developing a fluidflow model according to some embodiments (e.g., such as the model(s)discussed above for blocks 212, 220, 214, 222, 304, 306, 314, 316, 356,360, 406, 412, 458. 464 of methods 200, 300, 350, 400, 450, etc.). Thus,the method 500 may be utilized to develop a fluid outflow model, a fluidinflow model, a fluid flow classification model, etc. as describedherein. In addition, the method 500 may be utilized to develop a fluidflow model that receives one or more frequency domain features, one ormore temperature features, or both as inputs.

The method 500 can comprise obtaining acoustic and/or temperature dataor signals from a plurality of fluid flow tests at block 502. The fluidflow tests may be constructed and carried out in a manner so as toprovide acoustic and/or temperature data relevant for the particularevent the fluid flow model is intended to identify and/or characterize.Thus, when performing method 500 to develop a fluid outflow model toidentify one or more fluid outflow or uptake locations and/or a flowrate (or flow rate or volume allocation) of fluid at one or more fluidoutflow or uptake locations such as described above, block 502 maycomprise obtaining acoustic and/or temperature data from a plurality offluid flow tests in which one or more of a plurality of fluids areemitted from a conduit at predetermined locations spanning a length ofthe conduit.

Alternatively, when performing method 500 to develop a fluid inflowmodel to identify one or more fluid inflow locations, one or more fluidflow test may be performed at block 502 in which one or more fluids of aplurality of fluids are introduced into a conduit at predeterminedlocations spanning a length of the conduit, wherein the plurality offluids comprise a hydrocarbon gas, a hydrocarbon liquid, an aqueousfluid, or a combination thereof, and wherein the acoustic and/ortemperature signal comprises acoustic and or temperature samples acrossa portion of the conduit. The one or more fluids of a plurality offluids can be introduced into a flowing fluid to determine the inflowsignatures for fluid(s) entering flow fluids. In some embodiments, theone or more fluids can be introduced in a relatively stagnant fluid.This may help to model the lower or lowest producing portion of the wellwhere no bulk fluid flow may be passing through the wellbore at thepoint at which the fluid enters the well. This may be tested to obtainthe signature of fluid inflow into a fluid within the wellbore that maynot be flowing.

The acoustic and/or temperature signal can be obtained at 502 by anysuitable method. In some embodiments, the acoustic and/or temperaturedata can be from field data where the data is verified by other testinstruments. In some embodiments, the acoustic and/or temperature signalis obtained from a sensor or sensors within or coupled to the conduitfor each inflow test of the plurality of inflow tests. The sensor(s) canbe disposed along the length of the conduit, and the acoustic and/ortemperature signal that is obtained can be indicative of an acousticand/or temperature source along a length of the conduit. The sensor(s)can comprise a fiber optic cable (or a plurality of fiber optic cables)disposed within the conduit, or in some embodiments, coupled to theconduit (e.g., on an outside of the conduit). The conduit can be acontinuous section of a tubular, and in some embodiments, the can bedisposed in a loop. While described as being a loop in somecircumstances, a single section of pipe or tubular can also be used withadditional piping used to return a portion of the fluid to the entranceof the conduit.

The configuration of the tubular test arrangement can be selected basedon an expected operating configuration. A generic test arrangement maycomprise a single tubular having one or more emission or injectionpoints. The acoustic and/or temperature sensor can be disposed withinthe tubular or coupled to an exterior of the tubular. In someembodiments, other arrangement such as pipe-in-pipe arrangementsdesigned to mimic a production tubular in a casing string can be usedfor the flow tests. The sensor(s) can be disposed within the inner pipe,in an annulus between the inner pipe and outer pipe, or coupled to anexterior of the outer pipe. The disposition of the sensor(s) and themanner in which it is coupled within the test arrangement can be thesame or similar to how it is expected to be disposed within a wellbore.Any number of testing arrangements and sensor placements can be used,thereby allowing for test data corresponding to an expected completionconfiguration. Over time, a library of configurations and resulting testdata can be developed to allow for future models to be developed basedon known, labeled data used to train models.

In some embodiments, the conduit comprises a flow loop, and the flowingfluid can selectively comprises an aqueous fluid, a hydrocarbon fluid, agas, or a combination thereof. The flowing fluid can selectivelycomprise a liquid phase, a multi-phase mixed liquid, or a liquid-gasmixed phase. In some embodiments, the flowing fluid within the conduitcan have a flow regime including, but not limited to, laminar flow,plugging flow, slugging flow, annular flow, turbulent flow, mist flow,bubble flow, or any combination thereof. Within these flow regimes, theflow and/or inflow can be time based. For example, a fluid inflow can belaminar over a first time interval followed by slugging flow over asecond time period, followed by a return to laminar or turbulent flowover a third time period. Thus, the specific flow regimes can beinterrelated and have periodic or non-periodic flow regime changes overtime.

Referring now to FIG. 10 (including FIGS. 10A and 10B), an assembly 1for performing fluid flow tests (e.g., such as those described hereinfor method 500) is shown. Assembly 1 comprises a conduit 5 into or ontowhich a sensor 2 (e.g., a fiber optic cable) is disposed. In someembodiments, the fiber optic cable 2 can be disposed within conduit 5.In some embodiments, the fiber optic cable 2 can be disposed along anoutside of the conduit 5, for example, coupled to an exterior of theconduit. The fiber optic cable 2 can be disposed along a length L ofconduit 5. In some embodiments, other types of sensors can be used suchas point source acoustic, vibration, or temperature sensors. A line 40may be configured for introducing fluid into a first end 6 of conduit 5.One or a plurality of emission or injection points 10 can be disposedalong length L of conduit 5. An assembly for performing fluid flow testscan comprise any number of emission or injection points. For example, anassembly for performing an outflow or inflow test according to thisdisclosure can comprise 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more emissionor injection points 10. For example, in the embodiment of FIG. 10A, fouremission or injection points 10A, 10B, 10C, and 10D are disposed alonglength L of conduit 5. By way of example, a length L of conduit 5 may bein a range of from about 10 to about 100 meters, from about 20 to about80 meters, or from about 30 to about 70 meters, for example, 30, 40, 45,50, 55, 60, 65, or 70 meters. The function of the points 10A, 10B, 10C,10C, 10D as emission or injection points during a fluid flow test may bedetermined by whether the points 10A, 10B, 10C, 10C, 10D are coupled toa higher pressure fluid source (e.g., that may deliver fluid through thepoints 10A, 10B, 10C, 10C, 10D into the conduit 5) or whether the points10A, 10B, 10C, 10C, 10D are coupled to a lower pressure sink (e.g., suchas one or more tanks that would take offloads of fluids from conduit viapoints 10A, 10B, 10C, 10C, 10D).

The emission or injection points may be positioned a spacing distanceapart with regard to each other and/or first end 6 and second end 7 ofconduit 5. The spacing distance can be selected based on a spatialresolution of the sensor system such that the injection points can bedistinguished from each other in the resulting testing data. When pointsource sensors are used, the type of sensors can be considered inselecting the spacing distance. The spacing distance may also beselected, at least in part, to be sufficient to allow for a desired flowregime to develop between injection points. In some embodiments, firstemission or injection point 10A can be positioned a spacing distance S1from first end 6 of conduit 5 and a second spacing S2 from secondemission or injection point 10B. Second emission or injection point 10Bcan be positioned a spacing distance S3 from third emission or injectionpoint 10C. Third emission or injection point 10C can be positioned aspacing distance S4 from a fourth emission or injection point 10D.Fourth emission or injection point 10D can be positioned a spacingdistance S5 from a transparent section 20 of conduit 5. Transparentsection 20 can be utilized to visually confirm the flow regime withinconduit 5. The visual appearance information can be recorded as part ofthe test data set. A Production Logging System (PLS) may be positionedwithin a spacing distance S6 of second end 7 of conduit 5 and operableto compare data received via sensor or fiber optic cable 2. In someembodiments, without limitation, the spacing distances between emissionor injection points (e.g., spacing distances S2, S3, and S4) are in arange of from about 2 to about 20 m, from about 2 to about 15 m, or fromabout 10 m to about 15 m apart. In some embodiments, the first and lastemission or injection points are at least 5, 6, 7, 8, 9, or 10 m from aclosest end (e.g., from first end 6 or second end 7) of conduit 5. Forexample, spacing distances S1 and S5 can be at least 5, 6, 7, 8, 9, or10 meters, in some embodiments.

The conduit 5 can be disposed at any angle, including any angle between,and including, horizontal to vertical. The angle of the conduit, alongwith the fluid composition and flow rates can affect the flow regimeswithin the conduit. For example, a gas phase may collect along a top ofa horizontally oriented conduit 5 as compared to a bubbling or sluggingflow in a vertical conduit. Thus, the flow regime can change based on anorientation of the conduit even with the same fluid flow rates andcompositions. The angle can be selected to represent those conditionsthat are being modeled to match those found in a wellbore, and the angleof the conduit can become part of the data obtained from the test setup.

Fluid can be injected into line 40 in any of the flow regimes notedherein, for example, laminar flow, plugging flow, slugging flow, annularflow, turbulent flow, mist flow, and/or bubble flow, which may bevisually confirmed through transparent section 20 of assembly 1. Theinjected fluid from line 40 can comprise a liquid phase, a multi-phasemixed liquid, and/or a liquid-gas mixed phase. The fluid flow tests caninclude various combinations of, pressures, flow rates, etc. of injectedfluid at line 40. Outflow and inflow tests can also be performed for theemission and injection of single phase or multiphase fluids (e.g.,hydrocarbon liquid and gas, hydrocarbon liquid and water, hydrocarbonliquid, water, and gas) from or into, respectively, the conduit 5.

In order to understand the variability in the measured signal fortesting purposes, the flow for each type of flow can be incremented overtime. For example, the emission or injection rate can be varied in stepsover a time period. As another example, the flow rate, fluid type, flowregime, etc. of the injected fluid 40 may be varied (e.g., in steps,continuously, etc.) over a period of time. Each rate of emission orinjection rate (or the rate of fluid injection 40) can be held constantover a time period sufficient to obtain a useable sample data set. Thetime period should be sufficient to identify variability in the signalat a fixed rate. For example, between about 1 minute and about 30minutes of data can be obtained at each stepped flow rate beforechanging the flow rate to a different emission or injection rate.

As depicted in the schematic of FIG. 10B, which is a schematic 3 showingwellbore depths corresponding to injection points of FIG. 10A, the fluidflow tests can be calibrated to a certain reservoir depth, for example,by adjusting the fiber optic signal for the test depth. For example,emission or injection points 10A, 10B, 10C, and 10D can correspond tooutflow or inflow depths D1, D2, D3, and D4, respectively. As anexample, a length of fiber optic cable can be used that corresponds totypical wellbore depths (e.g., 3,000 m to 10,000 m, etc.). The resultingacoustic and/or temperature signals obtained by the fiber optic cable(or plurality of fiber optic cables) can then represent or beapproximations of acoustic and/or temperature signals received underwellbore conditions. During the flow tests, acoustic and/or temperaturedata can be obtained under known flow conditions. The resulting acousticdata can then be used as training and/or test data for purposes ofpreparing the fluid flow model. For example, a first portion of the datacan be used with machine learning techniques to train the fluid flowmodel, and a second portion of the data can be used to verify theresults from the fluid flow model once it is developed.

Referring again to FIG. 9 , in some embodiments, the test data obtainedfrom the flow apparatus of FIG. 10 may be utilized to obtain theacoustic data at 502 for method 500. Next, method 500 may comprisedetermining one or more frequency domain features and/or temperaturefeatures for each of the plurality of fluid flow tests at 504, andtraining the fluid flow model using the one or more frequency domainfeatures and/or the temperature features for a plurality of the testsand the predetermined locations at 506. The training of the fluid flowmodel can use machine learning, including any supervised or unsupervisedlearning approach. For example, the fluid flow model can be a neuralnetwork, a Bayesian network, a decision tree, a logistical regressionmodel, a normalized logistical regression model, k-means clustering orthe like.

In some embodiments, the fluid flow model can be developed and trainedusing a logistic regression model. As an example for training of a modelused to determine the presence or absence of a hydrocarbon gas phase(e.g., such as discussed above for block 218 of method 200), thetraining of the fluid flow model at 506 can begin with providing the oneor more frequency domain features and/or temperature features (includingany frequency domain features and/or temperature features notedhereinabove as well as combinations, transformations, and/or variantsthereof) to the logistic regression model corresponding to one or morefluid flow tests where the one or more fluids comprise a hydrocarbongas. The one or more frequency domain features and/or temperaturefeatures can be provided to the logistic regression model correspondingto one or more fluid flow tests where the one or more fluids do notcomprise a hydrocarbon gas. A first multivariate model can be determinedusing the one or more frequency domain features and/or temperaturefeatures as inputs. The first multivariate model can define arelationship between a presence and an absence of the hydrocarbon gas inthe one or more fluids.

A similar training protocol can be carried out to train the model (orother models) to define a relationship between a presence and absence ofaqueous fluid and hydrocarbon liquids. For instance, for a fluid flowmodel used to determine a fluid outflow location within a wellbore(e.g., such as discussed above for block 214 of method 10), the trainingof the fluid flow model at 306 can begin by providing the one or more ofthe plurality of frequency domain features (including any frequencydomain features noted herein above as well as combinations,transformation, and/or variants thereof) to the logistic regressionmodel corresponding to one or more outflow tests utilizing an injectionfluid of interest (e.g., water). A first multivariate model can bedetermined using the one or more frequency domain features and/ortemperature features as inputs. The first multivariate model can definea relationship between a presence and an absence of the fluid outflow atlocation of interest.

In the fluid flow model, the multivariate model equations can use thefrequency domain features, the temperature features or combinations ortransformations thereof to determine when a specific fluid, flow regime,and/or flow rate range is present. The multivariate model can define athreshold, decision point, and/or decision boundary having any type ofshapes such as a point, line, surface, or envelope between the presenceand absence of the specific fluid or flow regime. In some embodiments,the multivariate model can be in the form of a polynomial, though otherrepresentations are also possible. When models such as neural networksare used, the thresholds can be based on node thresholds within themodel. As noted herein, the multivariate model is not limited to twodimensions (e.g., two frequency domain features or two variablesrepresenting transformed values from two or more frequency domainfeatures), and rather can have any number of variables or dimensions indefining the threshold between the presence or absence of the fluid,flow regime, and/or flow rate range. When used, the detected values canbe used in the multivariate model, and the calculated value can becompared to the model values. The presence of the fluid, flow regime,and/or flow rate range can be indicated when the calculated value is onone side of the threshold and the absence of the fluid, flow regime,and/or flow rate range can be indicated when the calculated value is onthe other side of the threshold. Thus, each multivariate model can, insome embodiments, represent a specific determination between thepresence or absence of a fluid, flow regime, and/or flow rate range.Different models, and therefore thresholds, can be used for each fluidand/or flow regime, and each multivariate model can rely on differentfrequency domain features or combinations or transformations offrequency domain features. Since the multivariate models definethresholds for the determination and/or identification of specificfluids, and/or different flow rate ranges for each specific fluid, themultivariate models and fluid flow model using such multivariate modelscan be considered to be event signatures for each type of fluid flowand/or inflow (including flow regimes, flow rate ranges, etc.).

Referring still to FIG. 9 , once the model is trained or developed at506, method 500 may proceed to validate or verify the fluid flowmodel(s) at 508. In some embodiments, the plurality of the tests usedfor training the fluid flow model at 506 can be a subset of theplurality of fluid flow tests from 502, and the tests used to validatethe model(s) at 508 can be another subset of the plurality of fluid flowtests from 502. In addition, in some embodiments the validation at 508may be carried out using the acoustic signals and/or temperature signalsfrom one or more tests and the predetermined locations of the one ormore tests.

Any of the systems and methods disclosed herein can be carried out on acomputer or other device comprising a processor (e.g., a desktopcomputer, a laptop computer, a tablet, a server, a smartphone, or somecombination thereof), such as the acquisition device 160 of FIG. 1 .FIG. 11 illustrates a computer system 550 suitable for implementing oneor more embodiments disclosed herein such as the acquisition device orany portion thereof. The computer system 550 includes a processor 552(which may be referred to as a central processor unit or CPU) that is incommunication with memory devices including secondary storage 554, readonly memory (ROM) 556, random access memory (RAM) 558, input/output(I/O) devices 560, and network connectivity devices 562. The processor552 may be implemented as one or more CPU chips.

It is understood that by programming and/or loading executableinstructions onto the computer system 550, at least one of the CPU 552,the RAM 558, and the ROM 556 are changed, transforming the computersystem 550 in part into a particular machine or apparatus having thenovel functionality taught by the present disclosure. It is fundamentalto the electrical engineering and software engineering arts thatfunctionality that can be implemented by loading executable softwareinto a computer can be converted to a hardware implementation bywell-known design rules. Decisions between implementing a concept insoftware versus hardware typically hinge on considerations of stabilityof the design and numbers of units to be produced rather than any issuesinvolved in translating from the software domain to the hardware domain.Generally, a design that is still subject to frequent change may bepreferred to be implemented in software, because re-spinning a hardwareimplementation is more expensive than re-spinning a software design.Generally, a design that is stable that will be produced in large volumemay be preferred to be implemented in hardware, for example in anapplication specific integrated circuit (ASIC), because for largeproduction runs the hardware implementation may be less expensive thanthe software implementation. Often a design may be developed and testedin a software form and later transformed, by well-known design rules, toan equivalent hardware implementation in an application specificintegrated circuit that hardwires the instructions of the software. Inthe same manner as a machine controlled by a new ASIC is a particularmachine or apparatus, likewise a computer that has been programmedand/or loaded with executable instructions may be viewed as a particularmachine or apparatus.

Additionally, after the system 550 is turned on or booted, the CPU 552may execute a computer program or application. For example, the CPU 552may execute software or firmware stored in the ROM 556 or stored in theRAM 558. In some cases, on boot and/or when the application isinitiated, the CPU 552 may copy the application or portions of theapplication from the secondary storage 554 to the RAM 558 or to memoryspace within the CPU 552 itself, and the CPU 552 may then executeinstructions of which the application is comprised. In some cases, theCPU 552 may copy the application or portions of the application frommemory accessed via the network connectivity devices 562 or via the I/Odevices 560 to the RAM 558 or to memory space within the CPU 552, andthe CPU 552 may then execute instructions of which the application iscomprised. During execution, an application may load instructions intothe CPU 552, for example load some of the instructions of theapplication into a cache of the CPU 552. In some contexts, anapplication that is executed may be said to configure the CPU 552 to dosomething, e.g., to configure the CPU 552 to perform the function orfunctions promoted by the subject application. When the CPU 552 isconfigured in this way by the application, the CPU 552 becomes aspecific purpose computer or a specific purpose machine.

The secondary storage 554 is typically comprised of one or more diskdrives or tape drives and is used for non-volatile storage of data andas an over-flow data storage device if RAM 558 is not large enough tohold all working data. Secondary storage 554 may be used to storeprograms which are loaded into RAM 558 when such programs are selectedfor execution. The ROM 556 is used to store instructions and perhapsdata which are read during program execution. ROM 556 is a non-volatilememory device which typically has a small memory capacity relative tothe larger memory capacity of secondary storage 554. The RAM 558 is usedto store volatile data and perhaps to store instructions. Access to bothROM 556 and RAM 558 is typically faster than to secondary storage 554.The secondary storage 554, the RAM 558, and/or the ROM 556 may bereferred to in some contexts as computer readable storage media and/ornon-transitory computer readable media.

I/O devices 560 may include printers, video monitors, electronicdisplays (e.g., liquid crystal displays (LCDs), plasma displays, organiclight emitting diode displays (OLED), touch sensitive displays, etc.),keyboards, keypads, switches, dials, mice, track balls, voicerecognizers, card readers, paper tape readers, or other well-known inputdevices.

The network connectivity devices 562 may take the form of modems, modembanks, Ethernet cards, universal serial bus (USB) interface cards,serial interfaces, token ring cards, fiber distributed data interface(FDDI) cards, wireless local area network (WLAN) cards, radiotransceiver cards that promote radio communications using protocols suchas code division multiple access (CDMA), global system for mobilecommunications (GSM), long-term evolution (LTE), worldwideinteroperability for microwave access (WiMAX), near field communications(NFC), radio frequency identity (RFID), and/or other air interfaceprotocol radio transceiver cards, and other well-known network devices.These network connectivity devices 562 may enable the processor 552 tocommunicate with the Internet or one or more intranets. With such anetwork connection, it is contemplated that the processor 552 mightreceive information from the network, or might output information to thenetwork (e.g., to an event database) in the course of performing theabove-described method steps. Such information, which is oftenrepresented as a sequence of instructions to be executed using processor552, may be received from and outputted to the network, for example, inthe form of a computer data signal embodied in a carrier wave.

Such information, which may include data or instructions to be executedusing processor 552 for example, may be received from and outputted tothe network, for example, in the form of a computer data baseband signalor signal embodied in a carrier wave. The baseband signal or signalembedded in the carrier wave, or other types of signals currently usedor hereafter developed, may be generated according to several knownmethods. The baseband signal and/or signal embedded in the carrier wavemay be referred to in some contexts as a transitory signal.

The processor 552 executes instructions, codes, computer programs,scripts which it accesses from hard disk, floppy disk, optical disk(these various disk based systems may all be considered secondarystorage 554), flash drive, ROM 556, RAM 558, or the network connectivitydevices 562. While only one processor 552 is shown, multiple processorsmay be present. Thus, while instructions may be discussed as executed bya processor, the instructions may be executed simultaneously, serially,or otherwise executed by one or multiple processors. Instructions,codes, computer programs, scripts, and/or data that may be accessed fromthe secondary storage 554, for example, hard drives, floppy disks,optical disks, and/or other device, the ROM 556, and/or the RAM 558 maybe referred to in some contexts as non-transitory instructions and/ornon-transitory information.

In an embodiment, the computer system 550 may comprise two or morecomputers in communication with each other that collaborate to perform atask. For example, but not by way of limitation, an application may bepartitioned in such a way as to permit concurrent and/or parallelprocessing of the instructions of the application. Alternatively, thedata processed by the application may be partitioned in such a way as topermit concurrent and/or parallel processing of different portions of adata set by the two or more computers. In an embodiment, virtualizationsoftware may be employed by the computer system 550 to provide thefunctionality of a number of servers that is not directly bound to thenumber of computers in the computer system 550. For example,virtualization software may provide twenty virtual servers on fourphysical computers. In an embodiment, the functionality disclosed abovemay be provided by executing the application and/or applications in acloud computing environment. Cloud computing may comprise providingcomputing services via a network connection using dynamically scalablecomputing resources. Cloud computing may be supported, at least in part,by virtualization software. A cloud computing environment may beestablished by an enterprise and/or may be hired on an as-needed basisfrom a third party provider. Some cloud computing environments maycomprise cloud computing resources owned and operated by the enterpriseas well as cloud computing resources hired and/or leased from a thirdparty provider.

In an embodiment, some or all of the functionality disclosed above maybe provided as a computer program product. The computer program productmay comprise one or more computer readable storage medium havingcomputer usable program code embodied therein to implement thefunctionality disclosed above. The computer program product may comprisedata structures, executable instructions, and other computer usableprogram code. The computer program product may be embodied in removablecomputer storage media and/or non-removable computer storage media. Theremovable computer readable storage medium may comprise, withoutlimitation, a paper tape, a magnetic tape, magnetic disk, an opticaldisk, a solid state memory chip, for example analog magnetic tape,compact disk read only memory (CD-ROM) disks, floppy disks, jump drives,digital cards, multimedia cards, and others. The computer programproduct may be suitable for loading, by the computer system 550, atleast portions of the contents of the computer program product to thesecondary storage 554, to the ROM 556, to the RAM 558, and/or to othernon-volatile memory and volatile memory of the computer system 550. Theprocessor 552 may process the executable instructions and/or datastructures in part by directly accessing the computer program product,for example by reading from a CD-ROM disk inserted into a disk driveperipheral of the computer system 550. Alternatively, the processor 552may process the executable instructions and/or data structures byremotely accessing the computer program product, for example bydownloading the executable instructions and/or data structures from aremote server through the network connectivity devices 562. The computerprogram product may comprise instructions that promote the loadingand/or copying of data, data structures, files, and/or executableinstructions to the secondary storage 554, to the ROM 556, to the RAM558, and/or to other non-volatile memory and volatile memory of thecomputer system 550.

In some contexts, the secondary storage 554, the ROM 556, and the RAM558 may be referred to as a non-transitory computer readable medium or acomputer readable storage media. A dynamic RAM embodiment of the RAM558, likewise, may be referred to as a non-transitory computer readablemedium in that while the dynamic RAM receives electrical power and isoperated in accordance with its design, for example during a period oftime during which the computer system 550 is turned on and operational,the dynamic RAM stores information that is written to it. Similarly, theprocessor 552 may comprise an internal RAM, an internal ROM, a cachememory, and/or other internal non-transitory storage blocks, sections,or components that may be referred to in some contexts as non-transitorycomputer readable media or computer readable storage media.

Having described various systems and methods herein, certain aspects caninclude:

In a first aspect, a method of monitoring fluid outflow along a wellborecomprises: obtaining an acoustic signal from a sensor within thewellbore, wherein the acoustic signal comprises acoustic samples acrossa portion of a depth of the wellbore; determining one or more frequencydomain features from the acoustic signal; and identifying one or morefluid outflow locations along the portion of the depth of the wellboreusing the one or more frequency domain features.

A second aspect can include the method of the first aspect, wherein theone or more frequency domain features comprises at least two differentfrequency domain features.

A third aspect can include the method of the first or second aspect,wherein the sensor comprises a fiber optic cable disposed within thewellbore.

A fourth aspect can include the method of the third aspect, wherein theone or more frequency domain features comprises at least one of: aspectral centroid, a spectral spread, a spectral roll-off, a spectralskewness, a root mean square (RMS) band energy, a total RMS energy, aspectral flatness, a spectral slope, a spectral kurtosis, a spectralflux, a spectral autocorrelation function, or a normalized variantthereof.

A fifth aspect can include the method of any one of the first to fourthaspects, further comprising: denoising the acoustic signal prior todetermining the one or more frequency domain features.

A sixth aspect can include the method of the fifth aspect, whereindenoising the acoustic signal comprises median filtering the acousticdata.

A seventh aspect can include the method of the sixth aspect, furthercomprising: calibrating the acoustic signal.

An eighth aspect can include the method of the seventh aspect, furthercomprising: normalizing the one or more frequency domain features priorto identifying the one or more outflow locations using the one or morefrequency domain features.

A ninth aspect can include the method of any one of the first to eighthaspects, wherein identifying the one or more fluid outflow locationscomprises: identifying a background fluid flow signature using theacoustic signal; and removing the background fluid flow signature fromthe acoustic signal prior to identifying the one or more fluid outflowlocations.

A tenth aspect can include the method of any one of the first to ninthaspects, wherein identifying the one or more fluid outflow locationscomprises: identifying one or more anomalies in the acoustic signalusing the one or more frequency domain features; and selecting depthintervals of the one or more anomalies as the one or more outflowlocations.

An eleventh aspect can include the method of the tenth aspect, whereinthe depth intervals comprise depth intervals between packers within thewellbore, wherein the packers are disposed within an annulus between atubular member and a wall of the wellbore, and wherein the one or moreoutflow locations comprise locations where fluid is flowing into theannulus from the tubular member.

A twelfth aspect can include the method of any one of the first toeleventh aspects, wherein the identifying the one or more fluid outflowlocations comprises identifying the one or more fluid outflow locationsusing a logistic regression model that comprises a multivariate modelhaving the one or more frequency domain features as inputs.

A thirteenth aspect can include the method of any one of the first totwelfth aspects, further comprising: determining an allocation of atotal fluid flow across the one or more fluid outflow locations usingthe one or more frequency domain features.

A fourteenth aspect can include the method of any one of the first tothirteenth aspects, wherein the sensor comprises a fiber optic-basedacoustic sensor.

In a fifteenth aspect, a system for monitoring fluid outflow along awellbore comprises: a processor; a memory; and an analysis programstored in the memory, wherein the analysis program is configured, whenexecuted on the processor, to: obtain an acoustic signal, wherein theacoustic signal is received from a sensor within a wellbore, wherein theacoustic signal comprises acoustic samples across a portion of a depthof the wellbore; determine one or more frequency domain features fromthe acoustic signal; and identify one or more fluid outflow locationsalong the portion of the depth of the wellbore using the one or morefrequency domain features.

A sixteenth aspect can include the system of the fifteenth aspect,wherein the one or more frequency domain features comprises at least twodifferent frequency domain features.

A seventeenth aspect can include the system of the fifteenth orsixteenth aspect, wherein the sensor comprises a fiber optic cabledisposed within the wellbore.

An eighteenth aspect can include the system of the seventeenth aspect,wherein the one or more frequency domain features comprises at least oneof: a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, a root mean square (RMS) band energy, a total RMSenergy, a spectral flatness, a spectral slope, a spectral kurtosis, aspectral flux, a spectral autocorrelation function, or a normalizedvariant thereof.

A nineteenth aspect can include the system of any one of the fifteenthto eighteenth aspects, wherein the analysis program is configured, whenexecuted on the processor, to denoise the acoustic signal prior todetermining the one or more frequency domain features.

A twentieth aspect can include the system of the nineteenth aspect,wherein the analysis program is configured, when executed on theprocessor, to denoise the acoustic signal by median filtering theacoustic data.

A twenty first aspect can include the system of the nineteenth ortwentieth aspect, wherein the analysis program is configured, whenexecuted on the processor, to calibrate the acoustic signal.

A twenty second aspect can include the system of the twenty firstaspect, wherein the analysis program is configured, when executed on theprocessor, to normalize the one or more frequency domain features priorto identifying the one or more outflow locations using the one or morefrequency domain features.

A twenty third aspect can include the system of any one of the fifteenthto twenty second aspects, wherein the analysis program is configured,when executed on the processor, to: identify a background fluid flowsignature using the acoustic signal; and remove the background fluidflow signature from the acoustic signal prior to identifying the one ormore fluid outflow locations.

A twenty fourth aspect can include the system of anyone of the fifteenthto twenty third aspects, wherein the analysis program is configured,when executed on the processor, to: identify one or more anomalies inthe acoustic signal using the one or more frequency domain features; andselect depth intervals of the one or more anomalies as the one or moreoutflow locations.

A twenty fifth aspect can include the system of the twenty fourthaspect, wherein the depth intervals comprise depth intervals betweenpackers within the wellbore, wherein the packers are disposed within anannulus between a tubular member and a wall of the wellbore, and whereinthe one or more outflow locations comprise locations where fluid isflowing into the annulus from the tubular member.

A twenty sixth aspect can include the system of any one of the fifteenthto twenty fifth aspects, wherein the analysis program is configured,when executed on the processor, to use a logistic regression model toidentify the one or more fluid outflow locations, wherein the logisticregression model uses the one or more frequency domain features asinputs.

A twenty seventh aspect can include the system of any one of thefifteenth to twenty sixth aspects, wherein the analysis program isconfigured, when executed on the processor, to: determine an allocationof a total fluid flow across the one or more fluid outflow locationsusing the one or more frequency domain features.

A twenty eighth aspect can include the system of any one of thefifteenth to twenty seventh aspects, wherein the sensor comprises afiber optic-based acoustic sensor.

In a twenty ninth aspect, a method of monitoring fluid outflow along awellbore comprises: obtaining an acoustic signal from a sensor withinthe wellbore, wherein the acoustic signal comprises acoustic samplesacross a portion of a depth of the wellbore; determining a plurality offrequency domain features from the acoustic signal, wherein theplurality of frequency domain features are obtained across a pluralityof depth intervals within the portion of the depth of the wellbore, andwherein the plurality of frequency domain features comprise at least twodifferent frequency domain features; identifying a plurality of fluidoutflows at a plurality of fluid outflow locations within the pluralityof depth intervals using the plurality of frequency domain features; anddetermining an allocation of a total fluid flow across each of theplurality of fluid outflows using the plurality of frequency domainfeatures.

A thirtieth aspect can include the method of the twenty ninth aspect,wherein identifying the plurality of fluid outflow locations comprises:providing the plurality of frequency domain features to a fluid outflowmodel, wherein the fluid outflow model comprises a logistic regressionmodel; and determining that the plurality of fluid outflows are presentat the plurality of fluid outflow locations based on an output from thefluid outflow model.

A thirty first aspect can include the method of the thirtieth aspect,wherein identifying the plurality of fluid outflow locations comprisesproviding at least a subset of the plurality of frequency domainfeatures as inputs to the fluid outflow model to determine when thefluid outflows are present within the wellbore.

A thirty second aspect can include the method of the thirty firstaspect, comprising removing a background signal from the acoustic signalprior to determining the plurality of frequency domain features.

A thirty third aspect can include the method of the thirty secondaspect, wherein the sensor comprises a fiber optic-based acousticsensor.

In a thirty fourth aspect, a method of monitoring an injection of fluidinto a subterranean formation comprises: obtaining one or more frequencydomain features from an acoustic signal originating within a wellboreextending into the subterranean formation; identifying one or more fluidoutflow locations within the wellbore using the one or more frequencydomain features; obtaining one or more temperature features from atemperature signal originating within the wellbore; and identifying oneor more fluid uptake locations within the subterranean formation usingthe temperature features within the wellbore.

A thirty fifth aspect can include the method of the thirty fourthaspect, comprising shutting in the wellbore before obtaining the one ormore temperature features.

A thirty sixth aspect can include the method of the thirty fifth aspect,wherein the wellbore comprises one or more packers disposed within anannulus between a tubular member and a wall of the wellbore, and whereinthe one or more outflow locations comprise locations where fluid isflowing into the annulus from the tubular member.

A thirty seventh aspect can include the method of any one of the thirtyfourth to thirty sixth aspects, wherein the one or more temperaturefeatures comprises one or more of: a depth derivative of temperaturewith respect to depth; a temperature excursion measurement, wherein thetemperature excursion measurement comprises a difference between atemperature reading at a first depth and a smoothed temperature readingover a depth range, wherein the first depth is within the depth range; abaseline temperature excursion, wherein the baseline temperatureexcursion comprises a derivative of a baseline excursion with depth,wherein the baseline excursion comprises a difference between a baselinetemperature profile and a smoothed temperature profile; a peak-to-peakvalue, wherein the peak-to-peak value comprises a derivative of apeak-to-peak difference with depth, wherein the peak-to-peak differencecomprises a difference between a peak high temperature reading and apeak low temperature reading with an interval; an autocorrelation,wherein the autocorrelation is a cross-correlation of the temperaturesignal with itself; a heat loss parameter; or a time-depth derivative, adepth-time derivative, or both.

A thirty eighth aspect can include the method of any one of the thirtyfourth to thirty seventh aspects, wherein the one or more frequencydomain features comprises at least one of: a spectral centroid, aspectral spread, a spectral roll-off, a spectral skewness, an RMS bandenergy, a total RMS energy, a spectral flatness, a spectral slope, aspectral kurtosis, a spectral flux, a spectral autocorrelation function,a normalized variant thereof, or any combination thereof.

A thirty ninth aspect can include the method of any one of the thirtyfourth to thirty eighth aspects, comprising: determining an allocationof a total volumetric flow across the one or more fluid outflowlocations using the one or more frequency domain features.

A fortieth aspect can include the method of any one of the thirty fourthto thirty ninth aspects, comprising: determining a temperature change atdepths associated with the one or more fluid uptake locations for a timeperiod; and determining an allocation of a total injected fluid volumeacross the one or more fluid uptake locations based on the temperaturechange for the period of time.

A forty first aspect can include the method of anyone of the thirtyfourth to fortieth aspects, comprising: determining an allocation of atotal injected fluid volume across the one or more update locationsusing the one or more temperature features.

A forty second aspect can include the method of any one of the thirtyfourth to forty first aspects, wherein obtaining the one or morefrequency domain features comprises receiving the acoustic signal from afiber optic-based distributed acoustic sensor within the wellbore.

A forty third aspect can include the method of the forty second aspect,wherein obtaining the one or more temperature features comprisesreceiving the temperature signal from a fiber optic-based distributedtemperature sensor within the wellbore.

A forty fourth aspect can include the method of the forty third aspect,wherein the fiber optic-based distributed acoustic sensor and the fiberoptic based distributed temperature sensor comprise a single fiber opticcable.

A forty fifth aspect can include the method of any one of the thirtyfourth to forty fourth aspects, wherein identifying the one or morefluid outflow locations comprises using the one or more frequency domainfeatures in a first model.

A forty sixth aspect can include the method of the forty fifth aspect,wherein identifying the one or more fluid uptake locations comprisesusing the one or more temperature features in a second model.

In a forth seventh aspect, a system for monitoring an injection of fluidinto a subterranean formation comprises: a processor; a memory; and ananalysis program stored in the memory, wherein the analysis program isconfigured, when executed on the processor, to: obtain one or morefrequency domain features from an acoustic signal originating within awellbore extending into the subterranean formation; identify one or morefluid outflow locations within the wellbore using the one or morefrequency domain features; obtain one or more temperature features froma temperature signal originating within the wellbore; and identify oneor more fluid uptake locations within the subterranean formation usingthe temperature features.

A forty eighth aspect can include the system of the forty seventhaspect, wherein the wellbore comprises one or more packers disposedwithin an annulus between a tubular member and a wall of the wellbore,and wherein the one or more outflow locations comprise locations wherefluid is flowing into the annulus from the tubular member.

A forty ninth aspect can include the system of the forty seventh orforty eighth aspect, wherein the one or more temperature featurescomprises one or more of: a depth derivative of temperature with respectto depth; a temperature excursion measurement, wherein the temperatureexcursion measurement comprises a difference between a temperaturereading at a first depth and a smoothed temperature reading over a depthrange, wherein the first depth is within the depth range; a baselinetemperature excursion, wherein the baseline temperature excursioncomprises a derivative of a baseline excursion with depth, wherein thebaseline excursion comprises a difference between a baseline temperatureprofile and a smoothed temperature profile; a peak-to-peak value,wherein the peak-to-peak value comprises a derivative of a peak-to-peakdifference with depth, wherein the peak-to-peak difference comprises adifference between a peak high temperature reading and a peak lowtemperature reading with an interval; an autocorrelation, wherein theautocorrelation is a cross-correlation of the temperature signal withitself; a heat loss parameter; or a time-depth derivative, a depth-timederivative, or both.

A fiftieth aspect can include the system of any one of the forty seventhto forty ninth aspects, wherein the one or more frequency domainfeatures comprises at least one of: a spectral centroid, a spectralspread, a spectral roll-off, a spectral skewness, an RMS band energy, atotal RMS energy, a spectral flatness, a spectral slope, a spectralkurtosis, a spectral flux, a spectral autocorrelation function, anormalized variant thereof, or any combination thereof.

A fifty first aspect can include the system of any one of the fortyseventh to fiftieth aspects, wherein the analysis program is configured,when executed on the processor, to: determining an allocation of a totalvolumetric flow across the one or more fluid outflow locations using theone or more frequency domain features.

A fifty second aspect can include the system of any one of the fortyseventh to fifty first aspects, wherein the analysis program isconfigured, when executed on the processor, to: determine a temperaturechange at depths associated with the one or more fluid uptake locationsfor a time period; and determine an allocation of a total injected fluidvolume across the one or more fluid uptake locations based on thetemperature change for the period of time.

A fifty third aspect can include the system of any one of the fortyseventh to fifty second aspects, wherein the analysis program isconfigured, when executed on the processor, to: determine an allocationof a total injected fluid volume across the one or more update locationsusing the one or more temperature features.

A fifty fourth aspect can include the system of any one of the fortyseventh to fifty third aspects, comprising a fiber optic-baseddistributed acoustic sensor within the wellbore, wherein the analysisprogram is configured, when executed by the processor, to obtain theacoustic signal from the fiber optic-based distributed acoustic sensor.

A fifty fifth aspect can include the system of the fifty fourth aspect,comprising a fiber optic-based distributed temperature sensor within thewellbore, wherein the analysis program is configured, when executed bythe processor, to obtain the temperature signal from the fiberoptic-based temperature sensor.

A fifty sixth aspect can include the system of the fifty fifth aspect,wherein the fiber optic-based distributed acoustic sensor and the fiberoptic-based distributed temperature sensor comprise a single fiber opticcable.

A fifty seventh aspect can include the system of any one of the fortyseventh to fifty sixth aspects, wherein the analysis program isconfigured, when executed on the processor, to identify the one or morefluid outflow locations by inputting the one or more frequency domainfeatures into a first model.

A fifty eighth aspect can include the system of the fifty seventhaspect, wherein the analysis program is configured, when executed on theprocessor, to identify the one or more fluid uptake locations byinputting the one or more temperature features into a second model.

In a fifty ninth aspect, a method of monitoring an injection of fluidinto a subterranean formation comprises: obtaining an acoustic signalfrom a fiber optic-based acoustic sensor within a wellbore extendinginto the subterranean formation; obtaining a plurality of frequencydomain features from the acoustic signal; identifying a plurality offluid outflow locations within the wellbore using the plurality of thefrequency domain features; obtaining a temperature signal from a fiberoptic-based temperature sensor within the wellbore; obtaining aplurality of temperature features from the temperature signal; andidentifying a plurality of fluid uptake locations within thesubterranean formation using the temperature features.

A sixtieth aspect can include the method of the fifty ninth aspect,comprising: determining an allocation of a total injected fluid volumeacross the plurality of fluid outflow locations using the plurality offrequency domain features.

A sixty first aspect can include the method of the sixtieth aspect,comprising: determining an allocation of the total injected fluid volumeacross the plurality of fluid uptake locations using the plurality oftemperature features.

A sixty second aspect can include the method of the sixty first aspect,comprising shutting in the well after obtaining the acoustic signal andbefore obtaining the temperature signal.

A sixty third aspect can include the method of the sixty second aspect,wherein the plurality of temperature features comprises one or more of:a depth derivative of temperature with respect to depth; a temperatureexcursion measurement, wherein the temperature excursion measurementcomprises a difference between a temperature reading at a first depthand a smoothed temperature reading over a depth range, wherein the firstdepth is within the depth range; a baseline temperature excursion,wherein the baseline temperature excursion comprises a derivative of abaseline excursion with depth, wherein the baseline excursion comprisesa difference between a baseline temperature profile and a smoothedtemperature profile; a peak-to-peak value, wherein the peak-to-peakvalue comprises a derivative of a peak-to-peak difference with depth,wherein the peak-to-peak difference comprises a difference between apeak high temperature reading and a peak low temperature reading with aninterval; an autocorrelation, wherein the autocorrelation is across-correlation of the temperature signal with itself; a heat lossparameter; or a time-depth derivative, a depth-time derivative, or both.

A sixty fourth aspect can include the method of the sixty third aspect,wherein the plurality of frequency domain features comprises at leasttwo of: a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, an RMS band energy, a total RMS energy, a spectralflatness, a spectral slope, a spectral kurtosis, a spectral flux, aspectral autocorrelation function, a normalized variant thereof, or anycombination thereof.

A sixty fifth aspect can include the method of the sixty fourth aspect,wherein obtaining an acoustic signal comprises obtaining the acousticsignal from a fiber optic cable, and wherein obtaining the temperaturesignal comprises obtaining the temperature signal from the fiber opticcable.

In a sixty sixth aspect, a method of monitoring fluid outflow along awellbore comprises: determining one or more temperature features from adistributed temperature signal originating in the wellbore; determiningone or more frequency domain features from an acoustic signaloriginating in the wellbore; and using the one or more temperaturefeatures and the one or more frequency domain features to identify oneor more fluid outflow locations along the wellbore.

A sixty seventh aspect can include the method of the sixty sixth aspect,wherein using the one or more temperature features and the one or morefrequency domain features comprises: using the one or more temperaturefeatures in a first fluid outflow model; using the one or more frequencydomain features in a second fluid outflow model; combining an outputfrom the first fluid outflow model and an output from the second fluidoutflow model to form a combined output; and identifying the one or morefluid outflow locations along the wellbore based on the combined output.

A sixty eighth aspect can include the method of the sixty seventhaspect, wherein the first fluid outflow model comprise one or moremultivariate models, and wherein the output from each multivariate modelof the one or more multivariate models comprises an indication of theone or more locations along the wellbore.

A sixty ninth aspect can include the method of the sixty eighth aspect,wherein the second fluid outflow model comprises a regression model, andwherein the output from the regression model comprises an indication ofa fluid outflow rate at the one or more locations along the wellbore.

A seventieth aspect can include the method of the sixty ninth aspect,wherein combining the output from the first fluid outflow model with theoutput from the second fluid outflow model comprises determining thecombined output as a function of: 1) the output from the first fluidoutflow model, and 2) the output from the second fluid outflow model.

A seventy first aspect can include the method of the seventieth aspect,further comprising determining an allocation of a total injected fluidflow into the wellbore across the one or more fluid outflow locationsbased on the combined output.

A seventy second aspect can include the method of any one of the sixtysixth to seventy first aspects, wherein the one or more temperaturefeatures comprise at least one of: a depth derivative of temperaturewith respect to depth, a temperature excursion measurement, wherein thetemperature excursion measurement comprises a difference between atemperature reading at a first depth and a smoothed temperature readingover a depth range, wherein the first depth is within the depth range; abaseline temperature excursion, wherein the baseline temperatureexcursion comprises a derivative of a baseline excursion with depth,wherein the baseline excursion comprises a difference between a baselinetemperature profile and a smoothed temperature profile; a peak-to-peakvalue, wherein the peak-to-peak value comprises a derivative of apeak-to-peak difference with depth, wherein the peak-to-peak differencecomprises a difference between a peak high temperature reading and apeak low temperature reading with an interval; an autocorrelation,wherein the autocorrelation is a cross-correlation of the temperaturesignal with itself; a heat loss parameter; or a time-depth derivative, adepth-time derivative, or both.

A seventy third aspect can include the method of any one of the sixtysixth to seventy second aspects, wherein the one or more frequencydomain features comprise at least one of: a spectral centroid, aspectral spread, a spectral roll-off, a spectral skewness, an RMS bandenergy, a total RMS energy, a spectral flatness, a spectral slope, aspectral kurtosis, a spectral flux, or a spectral autocorrelationfunction.

A seventy fourth aspect can include the method of any one of the sixtysixth to seventy third aspects, comprising obtaining the distributedtemperature signal from a fiber optic-based temperature sensor withinthe wellbore.

A seventy fifth aspect can include the method of any one of the sixtysixth to seventy fourth aspects, comprising obtaining the acousticsignal from a fiber optic-based acoustic sensor within the wellbore.

In a seventy sixth aspect, a system for monitoring fluid outflow along awellbore comprises: a processor; a memory; and an analysis programstored in the memory, wherein the analysis program is configured, whenexecuted on the processor, to: receive a distributed temperature signaland an acoustic signal, wherein the distributed temperature sensingsignal and the acoustic signal originated within the wellbore; determineone or more temperature features from the distributed temperaturesensing signal; determine one or more frequency domain features from theacoustic signal; and identify one or more fluid outflow locations alongthe wellbore using the one or more temperature features and the one ormore frequency domain features.

A seventy seventh aspect can include the system of the seventy sixthaspect, wherein the analysis program is configured, when executed on theprocessor, to: use the one or more temperature features in a first fluidoutflow model; use the one or more frequency domain features in a secondfluid outflow model; combine an output from the first fluid outflowmodel and an output from the second fluid outflow model to form acombined output; and identify the one or more fluid outflow locationsalong the wellbore based on the combined output.

A seventy eighth aspect can include the system of the seventy seventhaspect, wherein the first fluid outflow model comprises one or moremultivariate models, and wherein the output from each multivariate modelof the one or more multivariate models comprises an indication of theone or more locations along the wellbore.

A seventy ninth aspect can include the system of the seventy seventh orseventy eighth aspect, wherein the second fluid outflow model comprisesa regression model, and wherein the output from the regression modelcomprises an indication of a fluid outflow rate at the one or morelocations along the wellbore.

An eightieth aspect can include the system of any one of the seventyseventh to seventh ninth aspects, wherein the analysis program isconfigured, when executed on the processor, to combine the output fromthe first fluid outflow model with the output from the second fluidoutflow model as a function of: 1) the output from the first fluidoutflow model, and 2) the output from the second fluid outflow model.

An eighty first aspect can include the system of any one of the seventyseventh to eightieth aspects, wherein the analysis program isconfigured, when executed on the processor, to determine an allocationof a total injected fluid flow into the wellbore across the one or morefluid outflow locations based on the combined output.

An eighty second aspect can include the system of any one of the seventysixth to eighty first aspects, wherein the one or more temperaturefeatures comprise at least one of: a depth derivative of temperaturewith respect to depth; a temperature excursion measurement, wherein thetemperature excursion measurement comprises a difference between atemperature reading at a first depth and a smoothed temperature readingover a depth range, wherein the first depth is within the depth range; abaseline temperature excursion, wherein the baseline temperatureexcursion comprises a derivative of a baseline excursion with depth,wherein the baseline excursion comprises a difference between a baselinetemperature profile and a smoothed temperature profile; a peak-to-peakvalue, wherein the peak-to-peak value comprises a derivative of apeak-to-peak difference with depth, wherein the peak-to-peak differencecomprises a difference between a peak high temperature reading and apeak low temperature reading with an interval; an autocorrelation,wherein the autocorrelation is a cross-correlation of the temperaturesignal with itself; a heat loss parameter; or a time-depth derivative, adepth-time derivative, or both.

An eighty third aspect can include the system of any one of the seventysixth to eighty second aspects, wherein the one or more frequency domainfeatures comprise at least one of: a spectral centroid, a spectralspread, a spectral roll-off, a spectral skewness, an RMS band energy, atotal RMS energy, a spectral flatness, a spectral slope, a spectralkurtosis, a spectral flux, or a spectral autocorrelation function.

An eighty fourth aspect can include the system of any one of the seventysixth to eighty third aspects, comprising a fiber optic-basedtemperature sensor within the wellbore, wherein the analysis program isconfigured, when executed on the processor, to obtain the distributedtemperature signal from the fiber optic-based temperature sensor.

An eighty fifth aspect can include the system of any one of the seventysixth to eighty fourth aspects, comprising a fiber optic-based acousticsensor within the wellbore, wherein the analysis program is configured,when executed on the processor, to obtain the acoustic signal from thefiber optic-based acoustic sensor.

In an eighty sixth aspect, a method of monitoring fluid outflow along awellbore comprises: determining one or more temperature features from adistributed temperature sensing signal originating in a wellbore,wherein the one or more temperature features comprise at least one of: adepth derivative of temperature with respect to depth, a temperatureexcursion measurement, a baseline temperature excursion, or apeak-to-peak value; determining one or more frequency domain featuresfrom an acoustic signal originated in the wellbore; and determining afluid outflow rate at one or more locations along the wellbore using theone or more temperature features and the one or more frequency domainfeatures.

An eighty seventh aspect can include the method of the eighty sixthaspect, wherein: the temperature excursion measurement comprises adifference between a temperature reading at a first depth and a smoothedtemperature reading over a depth range, wherein the first depth iswithin the depth range; the baseline temperature excursion comprises aderivative of a baseline excursion with depth, wherein the baselineexcursion comprises a difference between a baseline temperature profileand a smoothed temperature profile, and the peak-to-peak value comprisesa derivative of a peak-to-peak difference with depth, wherein thepeak-to-peak difference comprises a difference between a peak hightemperature reading and a peak low temperature reading with an interval.

An eighty eighth aspect can include the method of the eighty sixth oreighty seventh aspect, wherein the one or more frequency domain featurescomprise at least one of: a spectral centroid, a spectral spread, aspectral roll-off, a spectral skewness, an RMS band energy, a total RMSenergy, a spectral flatness, a spectral slope, a spectral kurtosis, aspectral flux, or a spectral autocorrelation function.

An eighty ninth aspect can include the method of any one of the eightysixth to eighty eighth aspects, wherein determining the fluid outflowrate at the one or more locations comprises: using the one or moretemperature features in a first fluid outflow model; using the one ormore frequency domain features in a second fluid outflow model;combining an output from the first fluid outflow model and an outputfrom the second fluid outflow model to form a combined output; anddetermining the fluid outflow rate at the one or more locations based onthe combined output.

A ninetieth aspect can include the method of the eighty ninth aspect,wherein the first fluid outflow model comprise one or more multivariatemodels, and wherein the output from each multivariate model of the oneor more multivariate model comprises an indication of the one or morelocations along the wellbore.

A ninety first aspect can include the method of the eighty ninth orninetieth aspect, wherein the second fluid outflow model comprises aregression model, and wherein the output from the regression modelcomprises an indication of a fluid outflow rate at the one or morelocations along the wellbore.

A ninety second aspect can include the method of any one of the eightysixth to ninety first aspects, wherein determining the fluid outflowrate at the one or more locations comprises determining an allocation atotal injected fluid flow into the wellbore across the one or more fluidoutflow locations based on the combined output.

In a ninety third aspect, a method of monitoring fluid injection into asubterranean formation comprises: obtaining a first acoustic signal froma first sensor within a first wellbore, wherein the first acousticsignal comprises acoustic samples across a portion of a depth of thefirst wellbore; determining one or more frequency domain features fromthe first acoustic signal; identifying one or more fluid outflowlocations within the first wellbore using the one or more frequencydomain features from the first acoustic signal; obtaining a secondacoustic signal from a second sensor within a second wellbore, whereinthe second acoustic signal comprises acoustic samples across a portionof a depth of the second wellbore; determining one or more frequencydomain features from the second acoustic signal; and identifying one ormore fluid inflow locations within the second wellbore using the one ormore frequency domain features from the second acoustic signal.

A ninety fourth aspect can include the method of the ninety thirdaspect, comprising: obtaining a distributed temperature signal from thefirst wellbore; obtaining one or more temperature features from thedistributed temperature signal; and identifying one or more fluid uptakelocations within the subterranean formation using the temperaturefeatures within the first wellbore.

A ninety fifth aspect can include the method of the ninety fourthaspect, comprising shutting in the first wellbore after obtaining thefirst acoustic signal and before obtaining the distributed temperaturesignal.

A ninety sixth aspect can include the method of the ninety fourth orninety fifth aspect, wherein the one or more temperature featurescomprise at least one of: a depth derivative of temperature with respectto depth, a temperature excursion measurement, wherein the temperatureexcursion measurement comprises a difference between a temperaturereading at a first depth and a smoothed temperature reading over a depthrange, wherein the first depth is within the depth range; a baselinetemperature excursion, wherein the baseline temperature excursioncomprises a derivative of a baseline excursion with depth, wherein thebaseline excursion comprises a difference between a baseline temperatureprofile and a smoothed temperature profile; a peak-to-peak value,wherein the peak-to-peak value comprises a derivative of a peak-to-peakdifference with depth, wherein the peak-to-peak difference comprises adifference between a peak high temperature reading and a peak lowtemperature reading with an interval; an autocorrelation, wherein theautocorrelation is a cross-correlation of the temperature signal withitself; a heat loss parameter; or a time-depth derivative, a depth-timederivative, or both.

A ninety seventh aspect can include the method of any one of the ninetyfourth to ninety sixth aspects, wherein identifying the one or morefluid outflow locations comprises inputting the one or more frequencydomain features from the first acoustic signal into a fluid outflowmodel, and wherein identifying the one or more fluid inflow locationscomprises inputting the one or more frequency domain features from thesecond acoustic signal into a fluid inflow model.

A ninety eighth aspect can include the method of the ninety seventhaspect, wherein identifying the one or more fluid uptake locationscomprises inputting the one or more temperature features into a fluiduptake model.

A ninety ninth aspect can include the method of the ninety eighthaspect, further comprising: determining a temperature change within thefirst wellbore at depths associated with the one or more fluid uptakelocations for a time period; and determining an allocation of a totalinjected fluid volume into the first wellbore among the one or morefluid uptake locations based on the temperature change for the period oftime.

A one hundredth aspect can include the method of any one of the ninetythird to ninety ninth aspects, further comprising: determining anindication of a fluid flow rate through the one or more fluid outflowlocations using the one or more frequency domain features from the firstacoustic signal; and determining an indication of a fluid flow ratethrough the one or more fluid inflow locations using the one or morefrequency domain features from the second acoustic signal.

A one hundred first aspect can include the method of the one hundredthaspect, wherein determining the indication of the fluid flow ratethrough the one or more fluid outflow locations comprises: determiningan allocation of a total injected fluid volume into the first wellboreacross the one or more fluid outflow locations using the one or morefrequency domain features from the first acoustic signal.

A one hundred second aspect can include the method of the one hundredthaspect, wherein determining the indication of the fluid flow ratethrough the one or more fluid inflow locations comprises: determining anallocation of a total fluid volume produced from the second wellboreacross the one or more fluid inflow locations using the one or morefrequency domain features from the second acoustic signal.

A one hundred third aspect can include the method of the one hundredthaspect, comprising: identifying at least one of a gas phase flow, anaqueous phase flow, or a hydrocarbon liquid phase flow through the oneor more inflow locations using the one or more the frequency domainfeatures from the second acoustic signal.

A one hundred fourth aspect can include the method of the one hundredthird aspect, wherein determining the indication of the fluid flow ratethrough the one or more fluid inflow locations comprises classifying aflow rate of the at least one of the gas phase flow, the aqueous phaseflow, or the hydrocarbon liquid phase flow using the plurality offrequency domain features from the second acoustic signal.

A one hundred fifth aspect can include the method of the one hundredfourth aspect, wherein classifying the flow rate comprises classifyingthe flow rate of the at least one of the gas phase flow, the aqueousphase flow, or the hydrocarbon liquid phase flow into a plurality ofpredetermined flow rate ranges using the plurality of frequency domainfeatures.

A one hundred sixth aspect can include the method of any one of theninety third to one hundred fifth aspects, wherein the one or morefrequency domain features of the first acoustic signal and the one ormore frequency domain features of the second acoustic signal comprise atleast one of: a spectral centroid, a spectral spread, a spectralroll-off, a spectral skewness, an RMS band energy, a total RMS energy, aspectral flatness, a spectral slope, a spectral kurtosis, a spectralflux, or a spectral autocorrelation function.

A one hundred seventh aspect can include the method of any one of theninety third to one hundred sixth aspects, wherein the first sensorcomprises a first fiber optic-based acoustic sensor within the firstwellbore, and the second sensor comprises a second fiber optic-basedacoustic sensor within the second wellbore.

A one hundred eighth aspect can include the method of any one of theninety third to one hundred seventh aspects, further comprising:adjusting one or more parameters of a reservoir model using the one ormore fluid inflow locations and the one or more outflow locations,wherein the first wellbore and the second wellbore are within areservoir represented by the reservoir model.

In a one hundred ninth aspect, a system for monitoring fluid injectioninto a subterranean formation comprises: a processor; a memory; and ananalysis program stored in the memory, wherein the analysis program isconfigured, when executed on the processor, to: obtain a first acousticsignal, wherein the first acoustic signal is received from a firstsensor within a first wellbore, wherein the first acoustic signalcomprises acoustic samples across a portion of a depth of the firstwellbore; determine one or more frequency domain features from the firstacoustic signal; identify one or more fluid outflow locations within thefirst wellbore using the one or more frequency domain features from thefirst acoustic signal; obtain a second acoustic signal, wherein thesecond acoustic signal is received from a second sensor within a secondwellbore, wherein the second acoustic signal comprises acoustic samplesacross a portion of a depth of the second wellbore; determine one ormore frequency domain features from the second acoustic signal; andidentify one or more fluid inflow locations within the second wellboreusing the one or more frequency domain features from the second acousticsignal.

A one hundred tenth aspect can include the system of the one hundredninth aspect, wherein the analysis program is configured, when executedon the processor, to: obtain a distributed temperature signal from thefirst wellbore; obtain one or more temperature features from thedistributed temperature signal; and identify one or more fluid uptakelocations within the subterranean formation using the temperaturefeatures within the first wellbore.

A one hundred eleventh aspect can include the system of the one hundredninth or one hundred tenth aspect, wherein the one or more temperaturefeatures comprise at least one of: a depth derivative of temperaturewith respect to depth, a temperature excursion measurement, wherein thetemperature excursion measurement comprises a difference between atemperature reading at a first depth and a smoothed temperature readingover a depth range, wherein the first depth is within the depth range; abaseline temperature excursion, wherein the baseline temperatureexcursion comprises a derivative of a baseline excursion with depth,wherein the baseline excursion comprises a difference between a baselinetemperature profile and a smoothed temperature profile; a peak-to-peakvalue, wherein the peak-to-peak value comprises a derivative of apeak-to-peak difference with depth, wherein the peak-to-peak differencecomprises a difference between a peak high temperature reading and apeak low temperature reading with an interval; an autocorrelation,wherein the autocorrelation is a cross-correlation of the temperaturesignal with itself; a heat loss parameter; or a time-depth derivative, adepth-time derivative, or both.

A one hundred twelfth aspect can include the system of any one of theone hundred ninth to one hundred eleventh aspects, wherein the analysisprogram is configured, when executed on the processor, to: input the oneor more frequency domain features from the first acoustic signal into afluid outflow model to identify the one or more fluid outflow locations,and input the one or more frequency domain features from the secondacoustic signal into a fluid inflow model to identify the one or morefluid inflow locations.

A one hundred thirteenth aspect can include the system of the onehundred twelfth aspect, wherein the analysis program is configured, whenexecuted on the processor, to input the one or more temperature featuresinto a fluid uptake model to identify the one or more fluid uptakelocations.

A one hundred fourteenth aspect can include the system of the onehundred thirteenth aspect, wherein the analysis program is configured,when executed on the processor, to: determine a temperature changewithin the first wellbore at depths associated with the one or morefluid uptake locations for a time period; and determine an allocation ofa total injected fluid volume into the first wellbore across the one ormore fluid uptake locations based on the temperature change for theperiod of time.

A one hundred fifteenth aspect can include the system of the one hundredfourteenth aspect, wherein the analysis program is configured, whenexecuted on the processor, to: determine an indication of a fluid flowrate through the one or more fluid outflow locations using the one ormore frequency domain features from the first acoustic signal; anddetermine an indication of a fluid flow rate through the one or morefluid inflow locations using the one or more frequency domain featuresfrom the second acoustic signal.

A one hundred sixteenth aspect can include the system of the one hundredfourteenth aspect, wherein the analysis program is configured, whenexecuted on the processor, to determine the indication of the fluid flowrate through the one or more fluid outflow locations by: determining anallocation of a total injected fluid volume into the first wellboreacross the one or more fluid outflow locations using the one or morefrequency domain features from the first acoustic signal.

A one hundred seventeenth aspect can include the system of the onehundred sixteenth aspect, wherein the analysis program is configured,when executed on the processor, to determine the indication of the fluidflow rate through the one or more fluid inflow locations by: determiningan allocation of a total fluid volume produced from the second wellboreacross the one or more fluid inflow locations using the one or morefrequency domain features from the second acoustic signal.

A one hundred eighteenth aspect can include the system of the onehundred sixteenth aspect, wherein the analysis program is configured,when executed on the processor, to: identifying at least one of a gasphase flow, an aqueous phase flow, or a hydrocarbon liquid phase flowthrough the one or more inflow locations using the one or more thefrequency domain features from the second acoustic signal.

A one hundred nineteenth aspect can include the system of the onehundred eighteenth aspect, wherein the analysis program is configured,when executed on the processor, to determine the indication of the fluidflow rate through the one or more fluid inflow locations by classifyinga flow rate of the at least one of the gas phase flow, the aqueous phaseflow, or the hydrocarbon liquid phase flow using the plurality offrequency domain features from the second acoustic signal.

A one hundred twentieth aspect can include the system of the one hundrednineteenth aspect, wherein the analysis program is configured, whenexecuted on the processor, to classify the flow rate of the at least oneof the gas phase flow, the aqueous phase flow, or the hydrocarbon liquidphase flow into a plurality of predetermined flow rate ranges using theplurality of frequency domain features.

A one hundred twenty first aspect can include the system of any one ofthe one hundred ninth to one hundred twentieth aspects, wherein the oneor more frequency domain features of the first acoustic signal and theone or more frequency domain features of the second acoustic signalcomprise at least one of: a spectral centroid, a spectral spread, aspectral roll-off, a spectral skewness, an RMS band energy, a total RMSenergy, a spectral flatness, a spectral slope, a spectral kurtosis, aspectral flux, or a spectral autocorrelation function.

A one hundred twenty second aspect can include the system of any one ofthe one hundred ninth to one hundred twenty first aspects, wherein thefirst sensor is a first fiber optic-based acoustic sensor within thefirst wellbore, and the second sensor is a second fiber optic-basedacoustic sensor within the second wellbore.

A one hundred twenty third aspect can include the system of any one ofthe one hundred ninth to one hundred twenty second aspects, wherein theanalysis program is configured: adjust one or more parameters of areservoir model using the one or more fluid inflow locations and the oneor more outflow locations, wherein the first wellbore and the secondwellbore are within a reservoir represented by the reservoir model.

In a one hundred twenty fourth aspect, a method of monitoring fluidinjection into a subterranean formation comprises: injecting a volume offluid into a first wellbore; obtaining a first acoustic signal from afirst sensor within the first wellbore, wherein the first acousticsignal comprises acoustic samples across a portion of a depth of thefirst wellbore; determining one or more frequency domain features fromthe first acoustic signal; identifying one or more fluid outflowlocations within the first wellbore using the one or more frequencydomain features from the first acoustic signal; obtaining a distributedtemperature signal from the first wellbore; obtaining one or moretemperature features from the distributed temperature signal;determining a portion of the volume of fluid that is received within aplurality of uptake locations within the subterranean formation usingthe one or more temperature features; obtaining a second acoustic signalfrom a second sensor within a second wellbore, wherein the secondacoustic signal comprises acoustic samples across a portion of a depthof the second wellbore; determining one or more frequency domainfeatures from the second acoustic signal; and identifying a presence ofat least one of a gas phase inflow, an aqueous phase inflow, or ahydrocarbon liquid phase inflow at one or more fluid inflow locationsusing the one or more frequency domain features from the second acousticsignal.

A one hundred twenty fifth aspect can include the method of the onehundred twenty fourth aspect, wherein obtaining the first acousticsignal comprises obtaining the first acoustic signal with a first fiberoptic cable within the first wellbore, and wherein obtaining the secondacoustic signal comprises obtaining the second acoustic signal with asecond fiber optic cable within the second wellbore.

A one hundred twenty sixth aspect can include the method of the onehundred twenty fourth or one hundred twenty sixth aspect, whereinobtaining the distributed temperature signal comprises obtaining thedistributed temperature signal with the first fiber optic cable.

A one hundred twenty seventh aspect can include the method of the onehundred twenty sixth aspect, comprising shutting in the first well afterobtaining the first acoustic signal and before obtaining the distributedtemperature signal.

A one hundred twenty eighth aspect can include the method of any one ofthe one hundred twenty fourth to one hundred twenty seventh aspects,wherein the one or more frequency domain features of the first acousticsignal and the one or more frequency domain features of the secondacoustic signal comprise at least one of: a spectral centroid, aspectral spread, a spectral roll-off, a spectral skewness, an RMS bandenergy, a total RMS energy, a spectral flatness, a spectral slope, aspectral kurtosis, a spectral flux, or a spectral autocorrelationfunction.

A one hundred twenty ninth aspect can include the method of any one ofthe one hundred twenty fourth to one hundred twenty eighth aspects,wherein the one or more temperature features comprise at least one of: adepth derivative of temperature with respect to depth, a temperatureexcursion measurement, wherein the temperature excursion measurementcomprises a difference between a temperature reading at a first depthand a smoothed temperature reading over a depth range, wherein the firstdepth is within the depth range; a baseline temperature excursion,wherein the baseline temperature excursion comprises a derivative of abaseline excursion with depth, wherein the baseline excursion comprisesa difference between a baseline temperature profile and a smoothedtemperature profile; a peak-to-peak value, wherein the peak-to-peakvalue comprises a derivative of a peak-to-peak difference with depth,wherein the peak-to-peak difference comprises a difference between apeak high temperature reading and a peak low temperature reading with aninterval; an autocorrelation, wherein the autocorrelation is across-correlation of the temperature signal with itself; a heat lossparameter; or a time-depth derivative, a depth-time derivative, or both.

In a one hundred thirtieth aspect, a method for monitoring fluidinjection into a subterranean formation comprises: injecting a fluidinto a wellbore extending into the subterranean formation; receiving anacoustic signal from a sensor within the wellbore, wherein the acousticsignal comprises acoustic samples across a portion of a depth of thewellbore; determining one or more frequency domain features from theacoustic signal; determining an allocation of an injected volume of thefluid across a plurality of outflow locations using the one or morefrequency domain features; receiving, at a first time, an indication ofa change in the allocation; storing a portion of the acoustic signal asa result of receiving the indication of the change, wherein the portionincludes the first time; and identifying an event within thesubterranean formation using the portion of the acoustic signal.

A one hundred thirty first aspect can include the method of the onehundred thirtieth aspect, wherein storing the portion of the acousticsignal comprises storing the portion of the acoustic signal associatedwith a selected depth or depth interval within the wellbore thatcorresponds with the indication of the change.

A one hundred thirty second aspect can include the method of the onehundred thirtieth or one hundred thirty first aspect, wherein the eventcomprises fracture formation within the subterranean formation.

A one hundred thirty third aspect can include the method of the onehundred thirty second aspect, wherein the change in the allocationcomprises a change that is greater than a predetermined threshold.

A one hundred thirty fourth aspect can include the method of any one ofthe one hundred thirtieth to one hundred thirty third aspects,comprising denoising the portion of the acoustic signal beforeidentifying the event.

A one hundred thirty fifth aspect can include the method of the onehundred thirty fourth aspect, wherein identifying the event comprisestriangulating the location of the fracture within the subterraneanformation based on the acoustic signal along the portion of the depth ofthe wellbore.

A one hundred thirty sixth aspect can include the method of any one ofthe one hundred thirtieth to one hundred thirty fifth aspects, whereinthe one or more frequency domain features comprise at least one of: aspectral centroid, a spectral spread, a spectral roll-off, a spectralskewness, an RMS band energy, a total RMS energy, a spectral flatness, aspectral slope, a spectral kurtosis, a spectral flux, or a spectralautocorrelation function.

A one hundred thirty seventh aspect can include the method of the onehundred thirty sixth aspect, comprising identifying the plurality offluid outflow locations using the one or more frequency domain features.

A one hundred thirty eighth aspect can include the method of any one ofthe one hundred thirtieth to one hundred thirty seventh aspects,comprising: shutting in the wellbore; determining a temperature changeover time at depths associated with one or more fluid uptake locationswithin the subterranean formation; and determining an allocation of theinjected volume across the one or more fluid uptake locations based onthe temperature change.

A one hundred thirty ninth aspect can include the method of the onehundred thirty eighth aspect, wherein determining the temperature changecomprises receiving a temperature signal from a fiber optic-basedtemperature sensor disposed within the wellbore.

A one hundred fortieth aspect can include the method of any one of theone hundred thirtieth to one hundred thirty ninth aspects, whereinreceiving the acoustic signal comprises receiving the acoustic signalfrom a fiber optic-based sensor within the wellbore.

A one hundred forty first aspect can include the method of the onehundred thirty second aspect, comprising: obtaining a second acousticsignal from a second sensor within a second wellbore extending in thesubterranean formation, wherein the second acoustic signal comprisesacoustic samples across a portion of a depth of the second wellbore;determining one or more frequency domain features from the secondacoustic signal; and confirming the fracture formation using the one ormore frequency domain features from the second acoustic signal.

A one hundred forty second aspect can include the method of any one ofthe one hundred thirtieth to one hundred forty first aspects, furthercomprising: adjusting one or more parameters of a reservoir model basedon the identification of the event within the subterranean formation.

In a one hundred forty third aspect, a system for monitoring fluidinjection into a subterranean formation comprises: a processor; amemory; and an analysis program stored in the memory, wherein theanalysis program is configured, when executed on the processor, to:receive an acoustic signal, wherein the acoustic signal is received froma sensor within a wellbore as a fluid is injected within the wellbore,wherein the acoustic signal comprises acoustic samples across a portionof a depth of the wellbore; determine one or more frequency domainfeatures from the acoustic signal; determine an allocation of aninjected volume of the fluid across a plurality of outflow locationsusing the one or more frequency domain features; receive, at a firsttime, an indication of a change in the allocation; store a portion ofthe acoustic signal as a result of receiving the indication of thechange, wherein the portion includes the first time; and identify anevent within the subterranean formation using the portion of theacoustic signal.

A one hundred forty fourth aspect can include the system of the onehundred forty third aspect, wherein the event comprises fractureformation within the subterranean formation.

A one hundred forty fifth aspect can include the system of the onehundred forty third or one hundred forty fourth aspect, wherein thechange in the allocation comprises a change that is greater than apredetermined threshold.

A one hundred forty sixth aspect can include the system of any one ofthe one hundred forty third to one hundred forty fifth aspects, whereinthe analysis program is configured, when executed on the processor, todenoise the portion of the acoustic signal before identifying the event.

A one hundred forty seventh aspect can include the system of any one ofthe one hundred forty fourth to one hundred forty sixth aspects, whereinthe analysis program is configured, when executed on the processor, tolocate the fracture within the subterranean formation based on theacoustic signal along the portion of the depth of the wellbore.

A one hundred forty eighth aspect can include the system of any one ofthe one hundred forty third to one hundred forty seventh aspects,wherein the one or more frequency domain features comprise at least oneof: a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, an RMS band energy, a total RMS energy, a spectralflatness, a spectral slope, a spectral kurtosis, a spectral flux, or aspectral autocorrelation function.

A one hundred forty ninth aspect can include the system of any one ofthe one hundred forty third to one hundred forty eighth aspects,comprising identifying the plurality of fluid outflow locations usingthe one or more frequency domain features.

A one hundred fiftieth aspect can include the system of the one hundredforty ninth aspect, wherein the analysis program is configured, whenexecuted on the processor, to: determine a temperature change over timeat depths associated with one or more fluid uptake locations within thesubterranean formation; and determine an allocation of the injectedvolume among the one or more fluid uptake locations based on thetemperature change.

A one hundred fifty first aspect can include the system of the onehundred fiftieth aspect, comprising a fiber optic-based temperaturesensor disposed within the wellbore; wherein the analysis program isconfigured, when executed on the processor, to: receive a temperaturesignal from the fiber optic-based temperature sensor; and determine thetemperature change based on the temperature signal.

A one hundred fifty second aspect can include the system of the onehundred fifty first aspect, comprising a fiber optic-based acousticsensor disposed within the wellbore, wherein the analysis program isconfigured, when executed on the processor, to receive the acousticsignal from the fiber optic-based acoustic sensor.

A one hundred fifty third aspect can include the system of any one ofthe one hundred forty third to one hundred fifty second aspects, whereinthe analysis program is configured, when executed on the processor, to:adjust one or more parameters of a reservoir model based on theidentification of the event within the subterranean formation.

In a one hundred fifty fourth aspect, a method for monitoring fluidinjection into a subterranean formation comprises: injecting a fluidinto a wellbore extending into the subterranean formation; receiving anacoustic signal from a sensor within the wellbore, wherein the acousticsignal comprises acoustic samples across a portion of a depth of thewellbore; determining one or more frequency domain features from theacoustic signal; determining an allocation of an injected volume of thefluid across a plurality of outflow locations using the one or morefrequency domain features; receiving, at a first time, an indication ofa change in the allocation; storing a portion of the acoustic signal asa result of receiving the indication of the change, wherein the portionincludes the first time; and identifying and locating a fracture withinthe subterranean formation using the portion of the acoustic signal.

A one hundred fifty fifth aspect can include the method of the onehundred fifty fourth aspect, comprising: shutting in the wellbore;determining a temperature change over time at depths associated with oneor more fluid uptake locations within the subterranean formation; anddetermining an allocation of the injected volume among the one or morefluid uptake locations based on the temperature change.

A one hundred fifty sixth aspect can include the method of the onehundred fifty fourth or one hundred fifty fifth aspect, whereinreceiving the acoustic signal comprises receiving the acoustic signalfrom a fiber optic-based sensor within the wellbore.

A one hundred fifty seventh aspect can include the method of the onehundred fifty sixth aspect, wherein determining the temperature changecomprises receiving a temperature signal from a fiber optic-basedtemperature sensor disposed within the wellbore.

A one hundred fifty eighth aspect can include the method of any one ofthe one hundred fifty fourth to the one hundred fifty seventh aspects,wherein the one or more frequency domain features comprise at least oneof: a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, an RMS band energy, a total RMS energy, a spectralflatness, a spectral slope, a spectral kurtosis, a spectral flux, or aspectral autocorrelation function.

A one hundred fifty ninth aspect can include the method of the onehundred fifty eighth aspect, wherein determining the allocation of theinjected volume comprises: determining an amplitude of fluid outflowthrough each of the plurality of fluid outflow locations; anddetermining the allocation based on the amplitude.

The embodiments disclosed herein include systems and methods forcharacterizing various fluid flows within, into, and out of asubterranean wellbore. In some embodiments, the embodiments disclosedherein may utilize a fiber optic cable to make distributed acousticand/or temperature measurements within the wellbore, and then usingthese measurement determine, identify, or otherwise characterize variousparameters, events, etc. of the fluid flowing within, into, or out ofthe wellbore during operations. Accordingly, through use of the systemsmethods disclosed herein, well operators are given enhanced indicationsand knowledge of the downhole environment so that downhole operationswith the wellbore may be improved.

While exemplary embodiments have been shown and described, modificationsthereof can be made by one skilled in the art without departing from thescope or teachings herein. The embodiments described herein areexemplary only and are not limiting. Many variations and modificationsof the systems, apparatus, and processes described herein are possibleand are within the scope of the disclosure. Accordingly, the scope ofprotection is not limited to the embodiments described herein, but isonly limited by the claims that follow, the scope of which shall includeall equivalents of the subject matter of the claims. Unless expresslystated otherwise, the steps in a method claim may be performed in anyorder. The recitation of identifiers such as (a), (b), (c) or (1), (2),(3) before steps in a method claim are not intended to and do notspecify a particular order to the steps, but rather are used to simplifysubsequent reference to such steps.

What is claimed is:
 1. A method of monitoring fluid outflow along awellbore, the method comprising: obtaining an acoustic signal from asensor within the wellbore, wherein the acoustic signal comprisesacoustic samples across a portion of a depth of the wellbore;determining one or more frequency domain features from the acousticsignal; and identifying one or more fluid outflow locations along theportion of the depth of the wellbore using the one or more frequencydomain features.
 2. The method of claim 1, wherein the one or morefrequency domain features comprises at least two different frequencydomain features.
 3. The method of claim 1, wherein the sensor comprisesa fiber optic cable disposed within the wellbore.
 4. The method of claim3, wherein the one or more frequency domain features comprises at leastone of: a spectral centroid, a spectral spread, a spectral roll-off, aspectral skewness, a root mean square (RMS) band energy, a total RMSenergy, a spectral flatness, a spectral slope, a spectral kurtosis, aspectral flux, a spectral autocorrelation function, or a normalizedvariant thereof.
 5. The method of claim 1, further comprising: denoisingthe acoustic signal prior to determining the one or more frequencydomain features.
 6. The method of claim 5, wherein denoising theacoustic signal comprises median filtering the acoustic data.
 7. Themethod of claim 6, further comprising: calibrating the acoustic signal.8. The method of claim 7, further comprising: normalizing the one ormore frequency domain features prior to identifying the one or moreoutflow locations using the one or more frequency domain features. 9.The method of claim 1, wherein identifying the one or more fluid outflowlocations comprises: identifying a background fluid flow signature usingthe acoustic signal; and removing the background fluid flow signaturefrom the acoustic signal prior to identifying the one or more fluidoutflow locations.
 10. The method of claim 1, wherein identifying theone or more fluid outflow locations comprises: identifying one or moreanomalies in the acoustic signal using the one or more frequency domainfeatures; and selecting depth intervals of the one or more anomalies asthe one or more outflow locations.
 11. The method of claim 10, whereinthe depth intervals comprise depth intervals between packers within thewellbore, wherein the packers are disposed within an annulus between atubular member and a wall of the wellbore, and wherein the one or moreoutflow locations comprise locations where fluid is flowing into theannulus from the tubular member.
 12. The method of claim 1, wherein theidentifying the one or more fluid outflow locations comprisesidentifying the one or more fluid outflow locations using a logisticregression model that comprises a multivariate model having the one ormore frequency domain features as inputs.
 13. The method of claim 1,further comprising: determining an allocation of a total fluid flowacross the one or more fluid outflow locations using the one or morefrequency domain features.
 14. The method of claim 1, wherein the sensorcomprises a fiber optic-based acoustic sensor.
 15. A system formonitoring fluid outflow along a wellbore, the system comprising: aprocessor; a memory; and an analysis program stored in the memory,wherein the analysis program is configured, when executed on theprocessor, to: obtain an acoustic signal, wherein the acoustic signal isreceived from a sensor within a wellbore, wherein the acoustic signalcomprises acoustic samples across a portion of a depth of the wellbore;determine one or more frequency domain features from the acousticsignal; and identify one or more fluid outflow locations along theportion of the depth of the wellbore using the one or more frequencydomain features.
 16. The system of claim 15, wherein the one or morefrequency domain features comprises at least two different frequencydomain features.
 17. The system of claim 15, wherein the sensorcomprises a fiber optic cable disposed within the wellbore.
 18. Thesystem of claim 17, wherein the one or more frequency domain featurescomprises at least one of: a spectral centroid, a spectral spread, aspectral roll-off, a spectral skewness, a root mean square (RMS) bandenergy, a total RMS energy, a spectral flatness, a spectral slope, aspectral kurtosis, a spectral flux, a spectral autocorrelation function,or a normalized variant thereof.
 19. The system of claim 15, wherein theanalysis program is configured, when executed on the processor, todenoise the acoustic signal prior to determining the one or morefrequency domain features.
 20. The system of claim 19, wherein theanalysis program is configured, when executed on the processor, todenoise the acoustic signal by median filtering the acoustic data. 21.The system of claim 19, wherein the analysis program is configured, whenexecuted on the processor, to calibrate the acoustic signal.
 22. Thesystem of claim 21, wherein the analysis program is configured, whenexecuted on the processor, to normalize the one or more frequency domainfeatures prior to identifying the one or more outflow locations usingthe one or more frequency domain features.
 23. The system of claim 15,wherein the analysis program is configured, when executed on theprocessor, to: identify a background fluid flow signature using theacoustic signal; and remove the background fluid flow signature from theacoustic signal prior to identifying the one or more fluid outflowlocations.
 24. The system of claim 15, wherein the analysis program isconfigured, when executed on the processor, to: identify one or moreanomalies in the acoustic signal using the one or more frequency domainfeatures; and select depth intervals of the one or more anomalies as theone or more outflow locations.
 25. The system of claim 24, wherein thedepth intervals comprise depth intervals between packers within thewellbore, wherein the packers are disposed within an annulus between atubular member and a wall of the wellbore, and wherein the one or moreoutflow locations comprise locations where fluid is flowing into theannulus from the tubular member.
 26. The system of claim 15, wherein theanalysis program is configured, when executed on the processor, to use alogistic regression model to identify the one or more fluid outflowlocations, wherein the logistic regression model uses the one or morefrequency domain features as inputs.
 27. The system of claim 15, whereinthe analysis program is configured, when executed on the processor, to:determine an allocation of a total fluid flow across the one or morefluid outflow locations using the one or more frequency domain features.28. The system of claim 15, wherein the sensor comprises a fiberoptic-based acoustic sensor.
 29. A method of monitoring fluid outflowalong a wellbore, the method comprising: obtaining an acoustic signalfrom a sensor within the wellbore, wherein the acoustic signal comprisesacoustic samples across a portion of a depth of the wellbore;determining a plurality of frequency domain features from the acousticsignal, wherein the plurality of frequency domain features are obtainedacross a plurality of depth intervals within the portion of the depth ofthe wellbore, and wherein the plurality of frequency domain featurescomprise at least two different frequency domain features; identifying aplurality of fluid outflows at a plurality of fluid outflow locationswithin the plurality of depth intervals using the plurality of frequencydomain features; and determining an allocation of a total fluid flowacross each of the plurality of fluid outflows using the plurality offrequency domain features.
 30. The method of claim 29, whereinidentifying the plurality of fluid outflow locations comprises:providing the plurality of frequency domain features to a fluid outflowmodel, wherein the fluid outflow model comprises a logistic regressionmodel; and determining that the plurality of fluid outflows are presentat the plurality of fluid outflow locations based on an output from thefluid outflow model.
 31. The method of claim 30, wherein identifying theplurality of fluid outflow locations comprises providing at least asubset of the plurality of frequency domain features as inputs to thefluid outflow model to determine when the fluid outflows are presentwithin the wellbore.
 32. The method of claim 31, comprising removing abackground signal from the acoustic signal prior to determining theplurality of frequency domain features.
 33. The method of claim 32,wherein the sensor comprises a fiber optic-based acoustic sensor.